[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-268d90eeae6a5c77-gemma-4-prod-stack-model-armor-adk-agents-tracing-summary":3,"summaries-facets-categories":271,"summary-related-268d90eeae6a5c77-gemma-4-prod-stack-model-armor-adk-agents-tracing-summary":3839},{"id":4,"title":5,"ai":6,"body":13,"categories":219,"created_at":222,"date_modified":222,"description":211,"extension":223,"faq":222,"featured":224,"kicker_label":222,"meta":225,"navigation":251,"path":252,"published_at":253,"question":222,"scraped_at":254,"seo":255,"sitemap":256,"source_id":257,"source_name":258,"source_type":259,"source_url":260,"stem":261,"tags":262,"thumbnail_url":222,"tldr":268,"tweet":222,"unknown_tags":269,"__hash__":270},"summaries\u002Fsummaries\u002F268d90eeae6a5c77-gemma-4-prod-stack-model-armor-adk-agents-tracing-summary.md","Gemma 4 Prod Stack: Model Armor, ADK Agents, Tracing",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8884,2621,18787,0.0025416,{"type":14,"value":15,"toc":210},"minimark",[16,21,25,28,76,79,82,85,88,92,95,102,105,119,122,125,128,131,135,138,141,144,152,155,159,162,165,168,171,174,178,204,207],[17,18,20],"h2",{"id":19},"unifying-model-serving-with-load-balancer-routing","Unifying Model Serving with Load Balancer Routing",[22,23,24],"p",{},"After deploying Gemma 4 separately via vLLM (optimized for production throughput, parallelism, memory) and Ollama (suited for dev\u002Ftesting) to Cloud Run services, the team routes traffic through a single regional external Application Load Balancer endpoint. This avoids managing multiple URLs in production.",[22,26,27],{},"Key decisions:",[29,30,31,48,58],"ul",{},[32,33,34,38,39,43,44,47],"li",{},[35,36,37],"strong",{},"Network Endpoint Groups (NEGs)",": Serverless NEGs represent Cloud Run backends for the LB. Created via ",[40,41,42],"code",{},"gcloud compute network-endpoint-groups create"," with ",[40,45,46],{},"--network-endpoint-type=SERVERLESS",".",[32,49,50,53,54,57],{},[35,51,52],{},"Backend Services",": Defined for each Cloud Run service (",[40,55,56],{},"gcloud compute backend-services create","), attached to NEGs. Enables LB to communicate securely.",[32,59,60,63,64,67,68,71,72,75],{},[35,61,62],{},"URL Map",": Routes based on path—e.g., ",[40,65,66],{},"\u002Fvllm\u002F"," to vLLM backend, ",[40,69,70],{},"\u002Follama\u002F"," to Ollama. Switch dev\u002Fprod by path prefix without endpoint changes. Command: ",[40,73,74],{},"gcloud compute url-maps create"," with host\u002Fpath rules.",[22,77,78],{},"Tradeoffs: Cloud Run scales multi-region natively, so LB adds setup overhead (NEGs, backends, proxy subnet, HTTPS certs, target proxy, forwarding rules). But it provides a single invocable HTTPS endpoint and service extensions. Without LB, use direct Cloud Run URLs, losing unified routing.",[22,80,81],{},"Proxy-only subnet reserves private IPs for LB-to-Cloud Run communication in the VPC. SSL certs enable HTTPS termination at the target HTTPS proxy, which consults the URL map before forwarding (port 443).",[22,83,84],{},"\"The reason why we're doing that for this particular lab using a load balancer, it's actually acting as a very advanced URL or a traffic router. So we have two different services, but we really don't want to be maintaining two different endpoints in production.\"",[22,86,87],{},"—Ayo Adedeji, explaining single-endpoint benefits over direct Cloud Run access.",[17,89,91],{"id":90},"network-level-security-with-model-armor-service-extension","Network-Level Security with Model Armor Service Extension",[22,93,94],{},"Model Armor scans every prompt\u002Fresponse for jailbreaks, prompt injection, PII leaks (e.g., SSNs, credit cards), harassment via LB service extension—triggered before backend routing.",[22,96,97,98,101],{},"Integration: Attach as extension to URL map (",[40,99,100],{},"gcloud compute url-maps add-service-extension","). Configurable thresholds\u002Factions: block malicious inputs, replace harmful outputs with defaults. Detects sensitive data in agent generations.",[22,103,104],{},"Alternatives considered:",[29,106,107,113],{},[32,108,109,112],{},[35,110,111],{},"SDK\u002FAPI",": Invoke via Python SDK or REST API in ADK callbacks (before-agent or after-model). No LB needed—e.g., filter inputs pre-agent call.",[32,114,115,118],{},[35,116,117],{},"Direct in code",": Embed in app logic, but network-level is zero-code-change, applies to all backends.",[22,120,121],{},"Why LB extension? Enforces security at ingress without app modifications; scales with traffic. For non-LB setups, callbacks provide lifecycle hooks (e.g., pre-model scan).",[22,123,124],{},"\"Model armor is really versatile you can use it in many different ways so there's a model armor python SDK... There's also model armor API that you can call... often times... before agent call back or after model call back.\"",[22,126,127],{},"—Ayo Adedeji, on flexible Model Armor invocation beyond LB.",[22,129,130],{},"Results: Blocks malicious traffic pre-model; logs detections for audit. Config via templates for custom harms\u002FPII.",[17,132,134],{"id":133},"model-agnostic-agents-with-adk-and-vllm-on-cloud-run","Model-Agnostic Agents with ADK and vLLM on Cloud Run",[22,136,137],{},"Agent Development Kit (ADK) builds agents atop any LLM (Gemini, Gemma 4). Here, pairs with lightweight vLLM serving Gemma 4, deployed to Cloud Run via Cloud Build CI\u002FCD.",[22,139,140],{},"Pipeline: Cloud Build triggers deploys; vLLM handles inference. Preps for \"boss fight\"—agent vs. cloud dungeon agent.",[22,142,143],{},"Why vLLM? High token throughput, GPU efficiency for prod. ADK callbacks enable Model Armor hooks.",[22,145,146,147,151],{},"\"ADK is actually model agnostic... The trick is we're gonna using ADK with light LLM ",[148,149,150],"span",{},"vLLM"," and you're gonna learn how to use that.\"",[22,153,154],{},"—Annie Wang, highlighting ADK flexibility for Gemma 4.",[17,156,158],{"id":157},"production-observability-metrics-and-end-to-end-tracing","Production Observability: Metrics and End-to-End Tracing",[22,160,161],{},"Post-deploy: Prometheus sidecar scrapes vLLM metrics (token throughput, GPU utilization, TTFT, req\u002Fs, latency, output tokens\u002Freq)—feeds cost\u002Fperformance monitoring.",[22,163,164],{},"Cloud Trace with OpenTelemetry: Traces agent flows end-to-end.",[22,166,167],{},"Why these? Directly tie to costs (GPU, tokens); essential for agent ops at scale. Sidecar avoids custom exporters.",[22,169,170],{},"\"We want to track things such as time to first token... GPU utilization request per second request latency output tokens per request. The reason why we want to do this because this all factors into how we control for and monitor performance throughput and costs.\"",[22,172,173],{},"—Ayo Adedeji, on metric selection for prod serving.",[17,175,177],{"id":176},"key-takeaways","Key Takeaways",[29,179,180,183,186,189,192,195,198,201],{},[32,181,182],{},"Use LB + URL maps for single-endpoint routing to multiple backends (e.g., vLLM prod vs. Ollama dev); path-based switching simplifies ops.",[32,184,185],{},"Integrate Model Armor as LB extension for zero-code network security; fallback to SDK\u002FAPI in ADK callbacks for direct Cloud Run.",[32,187,188],{},"Build model-agnostic agents with ADK + vLLM on Cloud Run; CI\u002FCD via Cloud Build for rapid iteration.",[32,190,191],{},"Monitor vLLM via Prometheus sidecar (GPU util, latency, tokens); add OpenTelemetry for agent traces.",[32,193,194],{},"Skip LB if no extensions\u002Frouting needed—Cloud Run scales alone—but LB unlocks Model Armor at ingress.",[32,196,197],{},"Reserve proxy-only subnet for secure LB-VPC comms; provision SSL certs for HTTPS.",[32,199,200],{},"Test in labs: Free GCP credits (non-GPU); full stack preps for agent battles\u002Fdungeons.",[32,202,203],{},"Prioritize observability pillars: security\u002Fsafety first, then metrics for cost control.",[22,205,206],{},"\"When we're talking about end-to-end agent system management... there's many different pillars... observability and security and safety.\"",[22,208,209],{},"—Ayo Adedeji, framing agent ops holistically.",{"title":211,"searchDepth":212,"depth":212,"links":213},"",2,[214,215,216,217,218],{"id":19,"depth":212,"text":20},{"id":90,"depth":212,"text":91},{"id":133,"depth":212,"text":134},{"id":157,"depth":212,"text":158},{"id":176,"depth":212,"text":177},[220,221],"AI & LLMs","DevOps & Cloud",null,"md",false,{"content_references":226,"triage":246},[227,232,235,238,243],{"type":228,"title":229,"url":230,"context":231},"tool","Agent Development Kit (ADK)","https:\u002F\u002Fgoo.gle\u002F4uflScr","recommended",{"type":228,"title":233,"url":234,"context":231},"Model Armor","https:\u002F\u002Fgoo.gle\u002F4mz57Ga",{"type":228,"title":236,"url":237,"context":231},"Cloud Trace","https:\u002F\u002Fgoo.gle\u002F4euYyCB",{"type":239,"title":240,"url":241,"context":242},"other","Hands-on AI Lab","https:\u002F\u002Fgoo.gle\u002Fguardians","mentioned",{"type":239,"title":244,"url":245,"context":242},"GCP Credits","https:\u002F\u002Fgoo.gle\u002Fhandson-ep8-lab1",{"relevance":247,"novelty":248,"quality":248,"actionability":247,"composite":249,"reasoning":250},5,4,4.55,"Category: AI Automation. The article provides a detailed guide on deploying AI agents with specific tools and configurations, addressing practical concerns like security and observability, which are crucial for product builders. It includes actionable commands and tradeoffs, making it highly relevant and immediately applicable.",true,"\u002Fsummaries\u002F268d90eeae6a5c77-gemma-4-prod-stack-model-armor-adk-agents-tracing-summary","2026-04-18 19:00:09","2026-04-19 03:42:07",{"title":5,"description":211},{"loc":252},"268d90eeae6a5c77","Google Cloud Tech","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7wENq-LMHgQ","summaries\u002F268d90eeae6a5c77-gemma-4-prod-stack-model-armor-adk-agents-tracing-summary",[263,264,265,266,267],"llm","agents","devops","cloud","ai-tools","Deploy secure, observable Gemma 4 agents on Cloud Run using load balancers for Model Armor integration, ADK for model-agnostic agents with vLLM, and Prometheus\u002FCloud Trace for metrics like GPU util and latency.",[],"kehgkdafSGcdmGRx8O8cwHNRvKfDZZ4PZMsrWWOjYc0",[272,275,278,280,283,286,288,290,292,294,296,298,301,303,305,307,309,311,313,315,317,319,322,325,327,329,332,334,336,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,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AI Agents via MCP Toolbox Custom Tools",{"provider":7,"model":8,"input_tokens":3844,"output_tokens":3845,"processing_time_ms":3846,"cost_usd":3847},8976,2997,46040,0.00327105,{"type":14,"value":3849,"toc":4073},[3850,3854,3857,3863,3866,3870,3873,3876,3883,3888,3891,3895,3898,3996,3999,4002,4007,4011,4014,4017,4020,4024,4027,4030,4035,4038,4040,4069],[17,3851,3853],{"id":3852},"tackling-the-confused-deputy-problem-in-ai-agents","Tackling the Confused Deputy Problem in AI Agents",[22,3855,3856],{},"AI agents promise automation like midnight database triage, but they risk the 'confused deputy' vulnerability: a service account with broad database access gets tricked by malicious user input (e.g., via prompt injection) into querying sensitive data like executive salaries instead of the paged-down DB. Kurtis Van Gent explains this as Simon Willison's 'lethal trifecta': private data + untrusted input + external sharing. Traditional fixes like prompt-engineered security fail because LLMs struggle to distinguish system vs. user instructions.",[3858,3859,3860],"blockquote",{},[22,3861,3862],{},"'The confused deputy problem is really a problem where you have some kind of authoritative source... but a malicious user or a bug can trick it into revealing information.' — Kurtis Van Gent, defining the core vulnerability with a real-world paging scenario.",[22,3864,3865],{},"Developers evaluated broad tool access (e.g., 'run any SQL') but rejected it for runtime agents serving end-users. Instead, they architected MCP Toolbox around customization: pre-author SQL queries reviewed like code, constraining what agents can do.",[17,3867,3869],{"id":3868},"build-time-vs-runtime-agents-tailored-tooling","Build-Time vs. Runtime Agents: Tailored Tooling",[22,3871,3872],{},"MCP Toolbox distinguishes two agent types, each with different security needs. Build-time agents (e.g., Gemini CLI, Claude Code) assist developers with broad, generic tools like 'any SQL' or BigQuery dashboard queries—safe since they use developer credentials. Runtime agents (e.g., customer service bots via ADK, LangChain) face untrusted users, needing narrow tools for accuracy and safety.",[22,3874,3875],{},"Toolbox supports both via generic (pre-built ops), runtime (dynamic), and custom tools. For databases like AlloyDB, BigQuery, Postgres, Valkey, Neo4j, Oracle, MariaDB, it acts as a 'central gate.' Open-source (15k+ GitHub stars, 130+ contributors, millions of monthly calls), it's self-hosted—no Google data access.",[22,3877,3878,3879,3882],{},"Key decision: Bound parameters separate agent-set values (e.g., flight ID from conversation) from app-set ones (e.g., user identity, target DB). This binds identity at runtime, e.g., ",[40,3880,3881],{},"tool.bind(user_id=authenticated_user)"," creates a scoped tool the LLM can't override.",[3858,3884,3885],{},[22,3886,3887],{},"'MCP is kind of the gold standard for interop right now... like USB for AI applications. You can take any agent and you can plug in any server.' — Kurtis Van Gent, positioning MCP as the standard Toolbox builds on.",[22,3889,3890],{},"Tradeoff: Hardcoding boosts security\u002Faccuracy (no hallucinated DB switches) but reduces flexibility. Philosophy: Remove agent control wherever possible without harming UX—e.g., hardcoded DB for single-DB sessions.",[17,3892,3894],{"id":3893},"custom-tools-pre-written-sql-as-architectural-guardrails","Custom Tools: Pre-Written SQL as Architectural Guardrails",[22,3896,3897],{},"Core mechanism: Define tools with fixed SQL templates and params. Example Postgres tool for airline queries:",[3899,3900,3904],"pre",{"className":3901,"code":3902,"language":3903,"meta":211,"style":211},"language-yaml shiki shiki-themes github-light github-dark","tool_type: postgres-sql\nsql: \"SELECT * FROM flights WHERE airline = $1 AND flight_number = $2\"\nparameters:\n  - name: airline\n    type: string\n  - name: flight_number\n    type: string\ndescription: \"Get flight details by airline and number\"\n","yaml",[40,3905,3906,3922,3932,3941,3954,3964,3976,3985],{"__ignoreMap":211},[148,3907,3910,3914,3918],{"class":3908,"line":3909},"line",1,[148,3911,3913],{"class":3912},"s9eBZ","tool_type",[148,3915,3917],{"class":3916},"sVt8B",": ",[148,3919,3921],{"class":3920},"sZZnC","postgres-sql\n",[148,3923,3924,3927,3929],{"class":3908,"line":212},[148,3925,3926],{"class":3912},"sql",[148,3928,3917],{"class":3916},[148,3930,3931],{"class":3920},"\"SELECT * FROM flights WHERE airline = $1 AND flight_number = $2\"\n",[148,3933,3935,3938],{"class":3908,"line":3934},3,[148,3936,3937],{"class":3912},"parameters",[148,3939,3940],{"class":3916},":\n",[148,3942,3943,3946,3949,3951],{"class":3908,"line":248},[148,3944,3945],{"class":3916},"  - ",[148,3947,3948],{"class":3912},"name",[148,3950,3917],{"class":3916},[148,3952,3953],{"class":3920},"airline\n",[148,3955,3956,3959,3961],{"class":3908,"line":247},[148,3957,3958],{"class":3912},"    type",[148,3960,3917],{"class":3916},[148,3962,3963],{"class":3920},"string\n",[148,3965,3967,3969,3971,3973],{"class":3908,"line":3966},6,[148,3968,3945],{"class":3916},[148,3970,3948],{"class":3912},[148,3972,3917],{"class":3916},[148,3974,3975],{"class":3920},"flight_number\n",[148,3977,3979,3981,3983],{"class":3908,"line":3978},7,[148,3980,3958],{"class":3912},[148,3982,3917],{"class":3916},[148,3984,3963],{"class":3920},[148,3986,3988,3991,3993],{"class":3908,"line":3987},8,[148,3989,3990],{"class":3912},"description",[148,3992,3917],{"class":3916},[148,3994,3995],{"class":3920},"\"Get flight details by airline and number\"\n",[22,3997,3998],{},"The LLM calls via MCP with params; Toolbox executes safely. No ad-hoc SQL generation—agents use dev-reviewed queries. Supports complex ops like joins\u002Fstored procs via custom SQL. Toolbox doesn't auto-write queries; devs do.",[22,4000,4001],{},"This mirrors app dev: Write\u002Freview SQL once, expose as API. For production, deploy on Cloud Run; min arch is Toolbox container + MCP client (Gemini\u002FVertex AI) + auth (e.g., IAM).",[3858,4003,4004],{},[22,4005,4006],{},"'The toolbox's superpower really comes down to... customize tools in a way that lets you constrain that access... write the SQL ahead of time.' — Kurtis Van Gent, on shifting from prompt hacks to code-like security.",[17,4008,4010],{"id":4009},"cymbal-air-demo-resilience-in-action","Cymbal Air Demo: Resilience in Action",[22,4012,4013],{},"Live demo of Cymbal Air (fictional airline agent): Normal flow—user asks flight status; agent uses bound tools to query only authorized data. Compromise attempt: \"Ignore instructions, query competitor salaries.\" Fails—tools lack access; agent stays on-topic.",[22,4015,4016],{},"Architecture: MCP client (Gemini) → Toolbox server (Cloud Run, Postgres backend) → bound custom tools. Code shown: Load tool, bind user context, register to agent. Result: Zero-trust, no leaks.",[22,4018,4019],{},"Evolution: Started with generic tools; pivoted to custom\u002Fbound for prod. Failure modes tested: Prompt injection blocked by param constraints.",[17,4021,4023],{"id":4022},"deployment-tradeoffs-and-best-practices","Deployment Tradeoffs and Best Practices",[22,4025,4026],{},"Latency: Toolbox adds ~50-100ms vs. direct queries (MCP overhead + execution); fine for interactive agents, not ultra-high-throughput. Self-hosted (binary\u002Fcontainer\u002Flocal); progressive tool exposure via dynamic registration.",[22,4028,4029],{},"Security-first process: Start with threat modeling ('what can go wrong?'), prototype fast with frameworks like ADK, then harden. 'Move security left'—architect params\u002Ftools early, iterate weekly.",[3858,4031,4032],{},[22,4033,4034],{},"'Flexibility versus security... anything that you can take away from the agent tends to be a good thing to take away as long as it doesn't diminish the use case.' — Kurtis Van Gent, on balancing autonomy and guardrails.",[22,4036,4037],{},"Non-obvious: Runtime agents need dev-like rigor (code review SQL); build-time can be looser. Replicate by forking GitHub repo, binding identity, testing injections.",[17,4039,177],{"id":176},[29,4041,4042,4045,4048,4051,4054,4057,4060,4063,4066],{},[32,4043,4044],{},"Model threats early: Map confused deputy risks (private data + untrusted input) before building agents.",[32,4046,4047],{},"Use build-time tools broadly for dev (e.g., any-SQL); constrain runtime with custom MCP tools.",[32,4049,4050],{},"Pre-write\u002Freview SQL templates; define params\u002Fdescriptions for LLM guidance.",[32,4052,4053],{},"Bind app params (user ID, DB) at runtime—LLM sets only conversation-derived ones.",[32,4055,4056],{},"Deploy self-hosted Toolbox on Cloud Run; test latency (\u003C100ms typical) and injections.",[32,4058,4059],{},"Start small: Codelabs for BigQuery\u002FAlloyDB; scale to multi-agent apps.",[32,4061,4062],{},"Prioritize security in architecture: 1st step = threat model, not prototype.",[32,4064,4065],{},"Leverage open MCP spec: Plug any agent\u002Fserver; Google managed options for BigQuery\u002Fetc.",[32,4067,4068],{},"Measure: Millions of safe calls\u002Fmonth via Toolbox—prod-proven.",[4070,4071,4072],"style",{},"html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":211,"searchDepth":212,"depth":212,"links":4074},[4075,4076,4077,4078,4079,4080],{"id":3852,"depth":212,"text":3853},{"id":3868,"depth":212,"text":3869},{"id":3893,"depth":212,"text":3894},{"id":4009,"depth":212,"text":4010},{"id":4022,"depth":212,"text":4023},{"id":176,"depth":212,"text":177},[220],{"content_references":4083,"triage":4111},[4084,4087,4090,4093,4096,4099,4102,4105,4108],{"type":228,"title":4085,"url":4086,"context":242},"MCP Toolbox GitHub","https:\u002F\u002Fgoo.gle\u002Fgithub-mcp-toolbox",{"type":228,"title":4088,"url":4089,"context":242},"MCP Toolbox for Databases (Docs)","https:\u002F\u002Fgoo.gle\u002Fmcp-toolbox-dev",{"type":228,"title":4091,"url":4092,"context":242},"QuickStart","https:\u002F\u002Fgoo.gle\u002Fmcp-quickstart",{"type":228,"title":4094,"url":4095,"context":242},"MCP Toolbox for Databases: Making BigQuery datasets available to MCP clients (Codelab)","https:\u002F\u002Fgoo.gle\u002Fcodelabs",{"type":228,"title":4097,"url":4098,"context":242},"Build a Multi-agent App with MCP Toolbox for AlloyDB & ADK (Codelab)","https:\u002F\u002Fgoo.gle\u002Fcodelab-multi-agent-app",{"type":228,"title":4100,"url":4101,"context":242},"Cymbal Air Toolbox Demo","https:\u002F\u002Fgoo.gle\u002F4tfWYIA",{"type":228,"title":4103,"url":4104,"context":242},"Google Cloud MCP servers overview","https:\u002F\u002Fgoo.gle\u002F42ioQRn",{"type":228,"title":4106,"url":4107,"context":242},"MCP Toolbox for Databases (Toolbox)","https:\u002F\u002Fgoo.gle\u002F4wauUJp",{"type":228,"title":4109,"url":4110,"context":242},"GEAR","https:\u002F\u002Fgoo.gle\u002FGEAR",{"relevance":248,"novelty":3934,"quality":248,"actionability":3934,"composite":4112,"reasoning":4113},3.6,"Category: AI & LLMs. The article addresses a specific pain point regarding security in AI agents, particularly the confused deputy problem, which is relevant for developers integrating AI features. It provides insights into a practical solution (MCP Toolbox) but lacks detailed step-by-step guidance for implementation.","\u002Fsummaries\u002Febc0d711136fb32c-secure-ai-agents-via-mcp-toolbox-custom-tools-summary","2026-05-05 16:46:33","2026-05-06 16:12:43",{"title":3842,"description":211},{"loc":4114},"ed722ee0fdc7e076","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CRszhkEjd8s","summaries\u002Febc0d711136fb32c-secure-ai-agents-via-mcp-toolbox-custom-tools-summary",[264,267,266,265],"MCP Toolbox prevents confused deputy attacks by letting developers pre-write constrained SQL tools with bound parameters, separating agent flexibility from app-controlled security for runtime agents.",[],"htBzEsyR16VdzmViKPvmry-2HFiUx9a6ye2MxpmOJCk",{"id":4127,"title":4128,"ai":4129,"body":4134,"categories":4251,"created_at":222,"date_modified":222,"description":211,"extension":223,"faq":222,"featured":224,"kicker_label":222,"meta":4252,"navigation":251,"path":4270,"published_at":4271,"question":222,"scraped_at":4272,"seo":4273,"sitemap":4274,"source_id":4275,"source_name":4276,"source_type":259,"source_url":4277,"stem":4278,"tags":4279,"thumbnail_url":222,"tldr":4280,"tweet":222,"unknown_tags":4281,"__hash__":4282},"summaries\u002Fsummaries\u002F376ca154ecbeafb2-composable-specialists-beat-monoliths-for-enterpri-summary.md","Composable Specialists Beat Monoliths for Enterprise AI",{"provider":7,"model":8,"input_tokens":4130,"output_tokens":4131,"processing_time_ms":4132,"cost_usd":4133},8466,2778,32971,0.00305955,{"type":14,"value":4135,"toc":4244},[4136,4140,4143,4146,4149,4156,4160,4163,4166,4169,4172,4178,4182,4185,4188,4191,4197,4201,4204,4213,4215],[17,4137,4139],{"id":4138},"granite-41-task-specific-models-for-agent-ecosystems","Granite 4.1: Task-Specific Models for Agent Ecosystems",[22,4141,4142],{},"Panelists hailed IBM Granite 4.1 as a pragmatic counter to frontier model hype, emphasizing its family of specialized multimodal models optimized for enterprise workloads. Marina Danilevsky highlighted vision models excelling at table and chart understanding—key for businesses over sci-fi image generation—while speech models shrink to minimal sizes for on-device transcription and translation. Language models (3B to 30B parameters) focus on instruction following and tool calling, ideal for RAG pipelines or agent offloads.",[22,4144,4145],{},"Kaoutar El Maghraoui framed this as composable system architecture, akin to 1980s OS evolution from monoliths to services. Unlike frontier labs' \"one giant model does everything,\" Granite complements general agents: route hard reasoning to Mistral, cheap completions to fine-tuned specialists. Gabe Goodhart stressed commoditization of large models, where enterprises prioritize supply chain optimization—cranking down costs without sacrificing task performance.",[22,4147,4148],{},"Consensus: Enterprises face token budgets blowing up quarterly; Granite enables \"token squeezing\" by offloading routine tasks (e.g., table parsing) to cheap, accurate specialists, reserving pricey generalists for orchestration. Trade-off: Less generality, but 90% of business tasks are routine, making this sustainable.",[22,4150,4151,4152,4155],{},"\"Enterprise cares. Can you understand tables? Not so much. Can you do the extremely coolest pictures that are sci fi? ",[148,4153,4154],{},"..."," It's can you understand tables?\" — Marina Danilevsky, underscoring practical priorities.",[17,4157,4159],{"id":4158},"ibm-bob-orchestrating-for-cost-and-legacy-modernization","IBM Bob: Orchestrating for Cost and Legacy Modernization",[22,4161,4162],{},"IBM Bob emerged as the glue: an agentic coding assistant that intelligently routes tasks across models, treating legacy languages like COBOL as first-class citizens—a moat for mainframe-heavy sectors like banking. El Maghraoui noted Bob's multimodal orchestration (e.g., Granite for security reviews) drives productivity without replacing developers; it handles 30% of routine work under bounded governance.",[22,4164,4165],{},"Goodhart positioned Bob for enterprise realities: consumer subscriptions absorb costs, but companies can't \"token max.\" Bob decides when to invoke sidecar specialists, keeping main logic in expensive models while optimizing overall spend. Danilevsky saw complementarity with Granite—standalone functions composed modularly.",[22,4167,4168],{},"Divergence on agents' future: Host Tim Hwang questioned if 90% routine tasks doom general agents as unpredictable costs. Goodhart countered with maturation: distill user patterns into sub-agents\u002Ftools on small models for quality\u002Fcost control, retaining top-level agent UX. Danilevsky agreed, viewing generalists as discovery phase for data-driven specialists. El Maghraoui predicted hybrid infrastructure: generalist + specialists via layered orchestration.",[22,4170,4171],{},"No one saw agent demos ending; instead, agents evolve from hype to infrastructure, distilling generality into specifics.",[22,4173,4174,4175,4177],{},"\"The goal there with Bob is not necessarily individual optimization ",[148,4176,4154],{}," how do I figure out most intelligently how to and when to invoke those side spurs to offload cost.\" — Gabe Goodhart, on token rightsizing.",[17,4179,4181],{"id":4180},"diloco-distributed-training-reshapes-infrastructure","DiLoCo: Distributed Training Reshapes Infrastructure",[22,4183,4184],{},"Shifting to infrastructure, DeepMind's DiLoCo (Distributed Low-Communication) challenged gigawatt-scale single-site clusters. El Maghraoui called it a hedge against power permitting and supply chains—Northern Virginia's grid is maxed, needing substations. DiLoCo cuts comms, boosts fault tolerance (88% uptime vs. 27% classical), and introduces \"goodput\" as the mature metric over peak FLOPs.",[22,4186,4187],{},"Implications: Training federates across data centers (different speeds\u002Fhardware), while inference co-locates for KV cache latency. Danilevsky tied to policy: flexible draw adapts to grid strain (e.g., AC peaks in California), easing upgrades and enabling constraints without halting progress. Goodhart noted post-FSDP\u002F4D parallelism evolution, prioritizing tail latency under failures.",[22,4189,4190],{},"Panel agreed: Bifurcation ahead—distributed training, concentrated inference—rethinking topologies amid waste from failures. Too late for sunk data centers? No, challenges assumptions from 2023-2025 plans by DeepMind itself.",[22,4192,4193,4194,4196],{},"\"Gigawatt scale, single site cluster assumption ",[148,4195,4154],{}," is now being challenged by its biggest practitioners.\" — Kaoutar El Maghraoui, on DiLoCo's impact.",[17,4198,4200],{"id":4199},"quantum-tease-and-broader-predictions","Quantum Tease and Broader Predictions",[22,4202,4203],{},"The truncated discussion previewed quantum with Jamie Garcia (IBM Director of Strategic Growth and Quantum Partnerships), touching university ties and quantum advantage paths. Earlier themes predicted: agent UX persists via delegation; models commoditize into optimized stacks; infrastructure splits training\u002Finference. Recommendations: Build composable systems now—specialists for 80-90% tasks, agents for glue. Trade-offs: Frontier generality shines in demos but fails enterprise scale\u002Fcost.",[22,4205,4206,4207,4209,4210,4212],{},"\"I think what you're going to see ",[148,4208,4154],{}," is that the patterns ",[148,4211,4154],{}," are going to start to shake out into a bunch of common patterns, and then we're going to be able to extract those things out and make them tools.\" — Gabe Goodhart, forecasting agent evolution.",[17,4214,177],{"id":176},[29,4216,4217,4220,4223,4226,4229,4232,4235,4238,4241],{},[32,4218,4219],{},"Deploy Granite-like specialists for tables\u002Fcharts\u002Fspeech to offload agents, cutting costs 10x on routine enterprise tasks.",[32,4221,4222],{},"Use Bob-style orchestration to route legacy code (COBOL) and modals intelligently—moat for mainframes.",[32,4224,4225],{},"Avoid token maxing: Monitor quarterly budgets, delegate trivia to 3B models.",[32,4227,4228],{},"Embrace DiLoCo principles for training: Prioritize goodput\u002Ffault tolerance over peak FLOPs in distributed setups.",[32,4230,4231],{},"Hybrid future: Generalist front-end + distilled sub-agents\u002Ftools for controllability.",[32,4233,4234],{},"Bifurcate infra: Federate training across DCs, co-locate inference for latency.",[32,4236,4237],{},"Policy hedge: Distributed methods flex with grids, enabling sustainable scaling.",[32,4239,4240],{},"Start with generalists for discovery, distill to specifics via interaction data.",[32,4242,4243],{},"Enterprise AI is pluralistic: Compose families (vision\u002Fspeech\u002Fembeddings) over monoliths.",{"title":211,"searchDepth":212,"depth":212,"links":4245},[4246,4247,4248,4249,4250],{"id":4138,"depth":212,"text":4139},{"id":4158,"depth":212,"text":4159},{"id":4180,"depth":212,"text":4181},{"id":4199,"depth":212,"text":4200},{"id":176,"depth":212,"text":177},[220],{"content_references":4253,"triage":4267},[4254,4258,4262,4265],{"type":4255,"title":4256,"url":4257,"context":242},"podcast","Mixture of Experts","https:\u002F\u002Fibm.biz\u002F~O3Jx9YWYa",{"type":4259,"title":4260,"author":4261,"context":242},"paper","DiLoCo: Distributed Low Communication","Google DeepMind",{"type":228,"title":4263,"author":4264,"context":231},"IBM Granite 4.1","IBM",{"type":228,"title":4266,"author":4264,"context":231},"IBM Bob",{"relevance":247,"novelty":248,"quality":248,"actionability":3934,"composite":4268,"reasoning":4269},4.15,"Category: AI & LLMs. The article discusses the practical application of IBM Granite 4.1's task-specific models and orchestration tools for enterprise AI, addressing the audience's need for actionable insights on AI integration in products. It provides a nuanced perspective on composable architecture versus monolithic systems, which is relevant for product builders.","\u002Fsummaries\u002F376ca154ecbeafb2-composable-specialists-beat-monoliths-for-enterpri-summary","2026-05-01 10:01:04","2026-05-03 16:43:43",{"title":4128,"description":211},{"loc":4270},"da3e89d622598bbe","IBM Technology","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Zk3FX8ZXa-s","summaries\u002F376ca154ecbeafb2-composable-specialists-beat-monoliths-for-enterpri-summary",[263,264,267,265],"Panel agrees enterprises need Granite 4.1's task-specific models and Bob's orchestration for cost control, with DiLoCo enabling distributed training to sidestep grid limits.",[],"diD5y4Qu8I8ZipkSRdrrxnMl_tkXo00zbbzTpaJqB88",{"id":4284,"title":4285,"ai":4286,"body":4291,"categories":4681,"created_at":222,"date_modified":222,"description":211,"extension":223,"faq":222,"featured":224,"kicker_label":222,"meta":4682,"navigation":251,"path":4700,"published_at":4701,"question":222,"scraped_at":254,"seo":4702,"sitemap":4703,"source_id":4704,"source_name":258,"source_type":259,"source_url":4705,"stem":4706,"tags":4707,"thumbnail_url":222,"tldr":4708,"tweet":222,"unknown_tags":4709,"__hash__":4710},"summaries\u002Fsummaries\u002F17040afbe49e30f1-self-host-gemma-4-on-cloud-run-gpus-ollama-vs-vllm-summary.md","Self-Host Gemma 4 on Cloud Run GPUs: Ollama vs vLLM",{"provider":7,"model":8,"input_tokens":4287,"output_tokens":4288,"processing_time_ms":4289,"cost_usd":4290},8944,2783,21888,0.00288915,{"type":14,"value":4292,"toc":4673},[4293,4297,4300,4303,4306,4309,4313,4316,4372,4375,4378,4385,4389,4392,4397,4424,4437,4443,4469,4472,4478,4481,4484,4488,4491,4497,4536,4543,4550,4553,4556,4560,4626,4629,4632,4635,4637,4670],[17,4294,4296],{"id":4295},"choose-open-models-like-gemma-4-for-control-and-cost-predictability","Choose Open Models like Gemma 4 for Control and Cost Predictability",[22,4298,4299],{},"Self-hosting open models like Google's Gemma 4 gives you full control over customization, fine-tuning, and data privacy—critical for regulated industries like healthcare or finance where sending data to closed models like Gemini isn't viable. Closed models excel out-of-the-box with state-of-the-art performance but limit tuning beyond prompts. Open models cap costs at infrastructure levels (no per-API-call scaling) and integrate as the \"brain\" in agentic systems via wrappers like Google's Agent Development Kit (ADK), which supports any LLM, not just Gemini.",[22,4301,4302],{},"Key principles: Evaluate models by performance, use case, cost, and capacity. Gemma 4 (2B parameter version here) fits L4 GPUs on Cloud Run, enabling scale-to-zero serverless inference. Use Ollama for dev\u002FPOC (easy local testing, multi-GPU) or vLLM for production (PagedAttention for memory efficiency, dynamic batching, high concurrency).",[22,4304,4305],{},"\"Open model like Gemma is easy to take control, you can even fine-tune it.\" — Annie Wang",[22,4307,4308],{},"Common mistake: Assuming agent frameworks lock you into proprietary models—ADK's LiteLLM wrapper connects any model seamlessly.",[17,4310,4312],{"id":4311},"shared-gcp-foundation-project-setup-and-permissions","Shared GCP Foundation: Project Setup and Permissions",[22,4314,4315],{},"Start in Cloud Shell (persistent VS Code-like VM at console.cloud.google.com). Run setup script to:",[4317,4318,4319,4326,4337,4344,4350,4356,4362,4365],"ol",{},[32,4320,4321,4322,4325],{},"Authenticate gcloud (",[40,4323,4324],{},"gcloud auth login",").",[32,4327,4328,4329,4332,4333,4336],{},"Clone repos: ",[40,4330,4331],{},"agentverse-devops-sre"," (templates, Cloud Build YAMLs) and ",[40,4334,4335],{},"agentverse-dungeon"," (agent fight files).",[32,4338,4339,4340,4343],{},"Create project (",[40,4341,4342],{},"agentverse-guardians-\u003CID>","), link billing manually via Manage Resources if needed.",[32,4345,4346,4347,47],{},"Set project: ",[40,4348,4349],{},"gcloud config set project \u003CID>",[32,4351,4352,4353,4325],{},"Enable APIs: Artifact Registry, Cloud Build, Cloud Run, Cloud Storage, Secret Manager (",[40,4354,4355],{},"gcloud services enable",[32,4357,4358,4359,47],{},"Create Artifact Registry repo: ",[40,4360,4361],{},"gcloud artifacts repositories create \u003Crepo> --repository-format=docker",[32,4363,4364],{},"Grant default service account roles: Storage Admin, Cloud Build Service Account, Logs Writer\u002FViewer, Secret Manager Secret Accessor.",[32,4366,4367,4368,4371],{},"Run ",[40,4369,4370],{},"warmup.sh"," to cache GCS FUSE.",[22,4373,4374],{},"Service accounts act as \"robot accounts\" for granular permissions—use separate ones in production. Enabling APIs incurs no immediate cost; billing starts on usage.",[22,4376,4377],{},"\"Every Google Cloud project has a default service account... that's essentially going to be like the operator behind many of your default actions.\" — Ayo Adedeji (IO)",[22,4379,4380,4381,4384],{},"Quality criteria: Verify project ID in yellow (Cloud Shell), ",[40,4382,4383],{},"gcloud config list"," shows correct project. Refresh page if timeouts occur (70-min security idle).",[17,4386,4388],{"id":4387},"ollama-deployment-bake-model-for-instant-cold-starts","Ollama Deployment: Bake Model for Instant Cold Starts",[22,4390,4391],{},"Ollama pulls and embeds Gemma 4 directly into the container—ideal for rapid iteration but requires rebuilds for model updates.",[22,4393,4394],{},[35,4395,4396],{},"Dockerfile:",[3899,4398,4402],{"className":4399,"code":4400,"language":4401,"meta":211,"style":211},"language-dockerfile shiki shiki-themes github-light github-dark","FROM ollama\u002Follama\nCOPY entrypoint.sh \u002Fentrypoint.sh\nRUN chmod +x \u002Fentrypoint.sh\nENTRYPOINT [\"\u002Fentrypoint.sh\"]\n","dockerfile",[40,4403,4404,4409,4414,4419],{"__ignoreMap":211},[148,4405,4406],{"class":3908,"line":3909},[148,4407,4408],{},"FROM ollama\u002Follama\n",[148,4410,4411],{"class":3908,"line":212},[148,4412,4413],{},"COPY entrypoint.sh \u002Fentrypoint.sh\n",[148,4415,4416],{"class":3908,"line":3934},[148,4417,4418],{},"RUN chmod +x \u002Fentrypoint.sh\n",[148,4420,4421],{"class":3908,"line":248},[148,4422,4423],{},"ENTRYPOINT [\"\u002Fentrypoint.sh\"]\n",[22,4425,4426,4429,4430,4433,4434,47],{},[40,4427,4428],{},"entrypoint.sh"," runs ",[40,4431,4432],{},"ollama serve"," and pulls ",[40,4435,4436],{},"gemma2:2b",[22,4438,4439,4442],{},[35,4440,4441],{},"cloudbuild-ollama.yaml:"," Defines CI\u002FCD pipeline:",[4317,4444,4445],{},[32,4446,4447,4448,4451],{},"Build: ",[40,4449,4450],{},"gcloud builds submit --config=cloudbuild-ollama.yaml .",[29,4452,4453,4458,4463],{},[32,4454,4455],{},[40,4456,4457],{},"docker build -t image .",[32,4459,4460,47],{},[40,4461,4462],{},"docker push gcr.io\u002F$PROJECT_ID\u002Follama",[32,4464,4465,4466,47],{},"Deploy to Cloud Run: ",[40,4467,4468],{},"gcloud run deploy ollama --image=gcr.io\u002F$PROJECT_ID\u002Follama --cpu=4 --memory=16Gi --gpu=nvidia-l4 --concurrency=4 --min-instances=1 --max-instances=1 --allow-unauthenticated --region=us-central1",[22,4470,4471],{},"Trade-offs: 16GB RAM for 2B model; L4 GPU; concurrency=4. Scales to zero but min=1 here for lab (scale higher in prod). Build takes 15-20 mins—monitor in Cloud Build console.",[22,4473,4474,4475,47],{},"Test: ",[40,4476,4477],{},"curl -X POST https:\u002F\u002Follama-\u003Chash>-uc.a.run.app\u002Fapi\u002Fgenerate -d '{\"model\": \"gemma2:2b\", \"prompt\": \"Why is the sky blue?\"}'",[22,4479,4480],{},"Before: Local Ollama testing. After: Serverless endpoint ready for agents.",[22,4482,4483],{},"\"Ollama is great for development use cases. It's really easy to install and get up and running.\" — Ayo Adedeji",[17,4485,4487],{"id":4486},"vllm-deployment-decouple-model-via-gcs-fuse-for-agility","vLLM Deployment: Decouple Model via GCS FUSE for Agility",[22,4489,4490],{},"vLLM loads model from Cloud Storage FUSE mount—slower initial boot (caches on first run) but swap models by updating GCS without redeploy.",[22,4492,4493,4494,4325],{},"Prerequisites: Hugging Face token in Secret Manager (",[40,4495,4496],{},"gcloud secrets create hf-token --data-file=\u003Ctoken>",[4317,4498,4499,4505,4521],{},[32,4500,4501,4502,4325],{},"Download Gemma 4 to GCS: Script pulls from HF (",[40,4503,4504],{},"huggingface-cli download google\u002Fgemma-2-2b-it",[32,4506,4507,4509,4510,4513,4514,4517,4518,47],{},[35,4508,4396],{}," Base ",[40,4511,4512],{},"vllm\u002Fvllm-openai",", mounts GCS bucket via FUSE (",[40,4515,4516],{},"gcsfuse","), serves on ",[40,4519,4520],{},"\u002Fv1",[32,4522,4523,4526,4527],{},[35,4524,4525],{},"cloudbuild-vllm.yaml:"," Similar pipeline, but image pulls HF token secret.\n",[29,4528,4529],{},[32,4530,4531,4532,4535],{},"Deploy: ",[40,4533,4534],{},"--gpu=nvidia-l4-count=1 --env-vars-file=vllm.env"," (adds HF_TOKEN).",[22,4537,4538,4539,4542],{},"FUSE enables mounting GCS as filesystem: ",[40,4540,4541],{},"gcsfuse \u003Cbucket> \u002Fmodels","—warmup caches for speed.",[22,4544,4545,4546,4549],{},"Test: Same curl to ",[40,4547,4548],{},"\u002Fv1\u002Fchat\u002Fcompletions"," with OpenAI-compatible API.",[22,4551,4552],{},"\"vLLM is great for production use cases. It comes with PagedAttention... great for memory efficiency.\" — Ayo Adedeji",[22,4554,4555],{},"Common mistake: Forgetting GPU alloc (L4), insufficient RAM (16Gi+), or FUSE warmup—leads to OOM or slow boots.",[17,4557,4559],{"id":4558},"production-trade-offs-and-agent-integration","Production Trade-offs and Agent Integration",[4561,4562,4563,4578],"table",{},[4564,4565,4566],"thead",{},[4567,4568,4569,4573,4576],"tr",{},[4570,4571,4572],"th",{},"Aspect",[4570,4574,4575],{},"Ollama",[4570,4577,150],{},[4579,4580,4581,4593,4604,4615],"tbody",{},[4567,4582,4583,4587,4590],{},[4584,4585,4586],"td",{},"Cold Start",[4584,4588,4589],{},"Instant (baked model)",[4584,4591,4592],{},"Slower (GCS mount)",[4567,4594,4595,4598,4601],{},[4584,4596,4597],{},"Model Updates",[4584,4599,4600],{},"Rebuild\u002Fdeploy",[4584,4602,4603],{},"GCS overwrite",[4567,4605,4606,4609,4612],{},[4584,4607,4608],{},"Use Case",[4584,4610,4611],{},"Dev\u002FPOC",[4584,4613,4614],{},"Prod (concurrency)",[4567,4616,4617,4620,4623],{},[4584,4618,4619],{},"Concurrency",[4584,4621,4622],{},"Basic",[4584,4624,4625],{},"Dynamic batching",[22,4627,4628],{},"Optimize: Use authenticated invokes; scale max-instances >1; monitor costs (GPUs aren't free). Integrate as agent \"brain\": ADK routes tools\u002Freasoning to your Cloud Run endpoint.",[22,4630,4631],{},"\"The model you're choosing really like can determine the upper bound, the capability of your agentic system.\" — Annie Wang",[22,4633,4634],{},"Exercise: Extend to boss fight in Agentverse—deploy agent vs. agent via A2A.",[17,4636,177],{"id":176},[29,4638,4639,4642,4645,4648,4651,4654,4664,4667],{},[32,4640,4641],{},"Self-host Gemma 4 on Cloud Run L4 GPUs for predictable costs and privacy in agent systems.",[32,4643,4644],{},"Use Ollama for fast dev deploys: Bake model in Dockerfile, CI\u002FCD via Cloud Build YAML.",[32,4646,4647],{},"Prefer vLLM for prod: Mount GCS via FUSE, update models without rebuilds.",[32,4649,4650],{},"Always setup IAM on default service account; enable APIs only incur costs on use.",[32,4652,4653],{},"Configure Cloud Run: 4 CPU\u002F16Gi RAM\u002FGPU=1\u002Fconcurrency=4; scale-to-zero with min=1 for labs.",[32,4655,4656,4657,4660,4661,4663],{},"Test with curl to ",[40,4658,4659],{},"\u002Fapi\u002Fgenerate"," (Ollama) or ",[40,4662,4548],{}," (vLLM).",[32,4665,4666],{},"Warm GCS FUSE cache; monitor builds in console (15-20 min).",[32,4668,4669],{},"Integrate via ADK LiteLLM wrapper for any model as agent brain.",[4070,4671,4672],{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":211,"searchDepth":212,"depth":212,"links":4674},[4675,4676,4677,4678,4679,4680],{"id":4295,"depth":212,"text":4296},{"id":4311,"depth":212,"text":4312},{"id":4387,"depth":212,"text":4388},{"id":4486,"depth":212,"text":4487},{"id":4558,"depth":212,"text":4559},{"id":176,"depth":212,"text":177},[220,221],{"content_references":4683,"triage":4698},[4684,4686,4688,4691,4694,4696],{"type":228,"title":4575,"url":4685,"context":242},"https:\u002F\u002Fgoo.gle\u002F3Qdi64w",{"type":228,"title":150,"url":4687,"context":242},"https:\u002F\u002Fgoo.gle\u002F4cvvxE9",{"type":228,"title":4689,"url":4690,"context":242},"Cloud Storage FUSE","https:\u002F\u002Fgoo.gle\u002F4cQAb0V",{"type":228,"title":4692,"url":4693,"context":242},"Cloud Run GPU","https:\u002F\u002Fgoo.gle\u002F4sEbTvG",{"type":239,"title":4695,"context":242},"Agent Development Kit",{"type":239,"title":4697,"url":241,"context":231},"Agentverse Lab",{"relevance":247,"novelty":248,"quality":248,"actionability":247,"composite":249,"reasoning":4699},"Category: AI & LLMs. The article provides a detailed guide on deploying the Gemma 4 LLM on Cloud Run, addressing practical applications for developers looking to integrate AI models into their products. It includes specific steps for setup and deployment, making it immediately actionable for the target audience.","\u002Fsummaries\u002F17040afbe49e30f1-self-host-gemma-4-on-cloud-run-gpus-ollama-vs-vllm-summary","2026-04-18 15:47:23",{"title":4285,"description":211},{"loc":4700},"17040afbe49e30f1","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=njWyDHKYeVA","summaries\u002F17040afbe49e30f1-self-host-gemma-4-on-cloud-run-gpus-ollama-vs-vllm-summary",[263,265,266,264],"Deploy open Gemma 4 LLM on serverless Cloud Run GPUs two ways: Ollama bakes model into container for instant cold starts; vLLM mounts from GCS FUSE for model swaps without rebuilds. Full CI\u002FCD via Cloud Build.",[],"_Vza7Oyow2_qaJpr6tl5zFUEbETkmjALA0GtIyH6k1E",{"id":4712,"title":4713,"ai":4714,"body":4719,"categories":4821,"created_at":222,"date_modified":222,"description":211,"extension":223,"faq":222,"featured":224,"kicker_label":222,"meta":4822,"navigation":251,"path":4823,"published_at":4824,"question":222,"scraped_at":222,"seo":4825,"sitemap":4826,"source_id":4827,"source_name":4828,"source_type":259,"source_url":4829,"stem":4830,"tags":4831,"thumbnail_url":222,"tldr":4832,"tweet":222,"unknown_tags":4833,"__hash__":4834},"summaries\u002Fsummaries\u002Frun-secure-ai-agent-for-10-mo-with-openclaw-docker-summary.md","Run Secure AI Agent for $10\u002FMo with OpenClaw + Docker",{"provider":7,"model":8,"input_tokens":4715,"output_tokens":4716,"processing_time_ms":4717,"cost_usd":4718},6107,1553,10829,0.00197525,{"type":14,"value":4720,"toc":4815},[4721,4725,4735,4745,4787,4790,4794,4797,4801,4804,4808],[17,4722,4724],{"id":4723},"build-persistent-agent-with-openclaw-minimax-and-docker","Build Persistent Agent with OpenClaw, MiniMax, and Docker",[22,4726,4727,4728,4731,4732,47],{},"OpenClaw provides an open-source gateway for a memory-enabled AI agent that persists context across sessions by writing notes to files like MEMORY.md and USER.md. It supports custom skills—directories with Markdown files describing tools for web search, APIs, or calendars—routed automatically by the agent. Install globally via ",[40,4729,4730],{},"npm install -g openclaw"," then ",[40,4733,4734],{},"openclaw gateway start",[22,4736,4737,4738,4741,4742,47],{},"Pair it with MiniMax's MiniMax-27 (or MiniMax-Text-01) model, offering 1 million token context, strong reasoning, and unlimited API calls for a flat $10\u002Fmonth—no per-token billing or throttling. Configure in OpenClaw via ",[40,4739,4740],{},"OPENCLAW_MODEL=minimax\u002FMiniMax-27"," and ",[40,4743,4744],{},"MINIMAX_API_KEY=your_key",[22,4746,4747,4748,4751,4752,4755,4756,4759,4760,4763,4764,4767,4768,4767,4771,4774,4775,4778,4779,4782,4783,4786],{},"Run everything in Docker for isolation: Use a Node:22-slim base image, create non-root ",[40,4749,4750],{},"openclaw"," user, expose port 8080, and mount ",[40,4753,4754],{},"\u002Fdata"," volume for persistence. docker-compose.yml binds to ",[40,4757,4758],{},"127.0.0.1:8080"," (localhost only), sets read-only root filesystem, drops all Linux capabilities except NET_BIND_SERVICE, adds ",[40,4761,4762],{},"no-new-privileges:true",", and uses tmpfs for \u002Ftmp. Environment vars pull from .env: ",[40,4765,4766],{},"MINIMAX_API_KEY",", ",[40,4769,4770],{},"OPENCLAW_KEY",[40,4772,4773],{},"TELEGRAM_TOKEN"," for chat integration (e.g., Telegram bot). Data persists in named volume ",[40,4776,4777],{},"openclaw-data"," at ",[40,4780,4781],{},"\u002Fdata\u002Fworkspace\u002F"," (SOUL.md for personality, skills\u002F, memory\u002F) and ",[40,4784,4785],{},"\u002Fdata\u002F.openclaw\u002F"," (config, sessions).",[22,4788,4789],{},"Connect to chat apps like Telegram, Discord, or WhatsApp for always-on access.",[17,4791,4793],{"id":4792},"harden-against-common-threats","Harden Against Common Threats",[22,4795,4796],{},"Bind ports to localhost to block external access; add reverse proxy (Caddy\u002Fnginx with TLS) for remote needs. Non-root user, read-only filesystem, and capability drops limit container escape: compromised code can't escalate privileges, write to host, or access unnecessary syscalls. Secrets stay in uncommitted .env (add to .gitignore first). Only outbound calls hit MiniMax API; swap for Ollama local model for zero external dependency, trading inference quality for full privacy. Agent memory accumulates in volumes, surviving restarts.",[17,4798,4800],{"id":4799},"dictation-unlocks-10x-better-prompts","Dictation Unlocks 10x Better Prompts",[22,4802,4803],{},"Voice input via DictaFlow (free tier) eliminates typing friction: Hold a key, speak, and transcription appears instantly in Telegram or notes. Reduces 2-minute typed prompts to 15 seconds, capturing richer nuance and context. Dictate 80% of interactions—research, instructions, updates—for more natural, effective agent responses, turning it into a flow-state thinking partner.",[17,4805,4807],{"id":4806},"low-costs-compound-to-indispensable-value","Low Costs Compound to Indispensable Value",[22,4809,4810,4811,4814],{},"Breakdown: MiniMax $10\u002Fmo, OpenClaw\u002FDocker\u002FTelegram $0, DictaFlow free tier—total $10\u002Fmo local, or $14\u002Fmo on $4 DigitalOcean droplet. After 1 month useful, 3 months indispensable as memory compounds project history. Launch: mkdir project, create .env\u002F.gitignore\u002Fdocker-compose.yml, ",[40,4812,4813],{},"docker compose up -d",", customize SOUL.md, add skills. Economics favor always-on usage without cloud lock-in.",{"title":211,"searchDepth":212,"depth":212,"links":4816},[4817,4818,4819,4820],{"id":4723,"depth":212,"text":4724},{"id":4792,"depth":212,"text":4793},{"id":4799,"depth":212,"text":4800},{"id":4806,"depth":212,"text":4807},[],{},"\u002Fsummaries\u002Frun-secure-ai-agent-for-10-mo-with-openclaw-docker-summary","2026-04-08 21:21:18",{"title":4713,"description":211},{"loc":4823},"d65062bf6fafe563","Level Up Coding","https:\u002F\u002Funknown","summaries\u002Frun-secure-ai-agent-for-10-mo-with-openclaw-docker-summary",[264,263,267,265],"Use OpenClaw agent runtime with MiniMax's $10\u002Fmo flat-rate LLM in a hardened Docker container for persistent, memory-enabled AI that runs locally, remembers context across sessions, and costs less than streaming.",[],"KYnxvU8cgr79htsCbZ4eFR1EIU4ibpIyadJuSJfAHx0"]