[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-build-multimodal-qwen-3-6-agents-with-thinking-too-summary":3,"summaries-facets-categories":419,"summary-related-build-multimodal-qwen-3-6-agents-with-thinking-too-summary":4824},{"id":4,"title":5,"ai":6,"body":13,"categories":384,"created_at":386,"date_modified":386,"description":31,"extension":387,"faq":386,"featured":388,"kicker_label":386,"meta":389,"navigation":401,"path":402,"published_at":403,"question":386,"scraped_at":404,"seo":405,"sitemap":406,"source_id":407,"source_name":408,"source_type":409,"source_url":410,"stem":411,"tags":412,"thumbnail_url":386,"tldr":416,"tweet":386,"unknown_tags":417,"__hash__":418},"summaries\u002Fsummaries\u002Fbuild-multimodal-qwen-3-6-agents-with-thinking-too-summary.md","Build Multimodal Qwen 3.6 Agents with Thinking & Tools",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",9570,2576,24918,0.00291115,{"type":14,"value":15,"toc":377},"minimark",[16,21,25,79,82,97,100,104,126,156,191,198,204,207,211,223,234,249,256,263,266,270,273,276,283,290,297,322,325,328,332,373],[17,18,20],"h2",{"id":19},"gpu-adaptive-loading-for-efficient-multimodal-inference","GPU-Adaptive Loading for Efficient Multimodal Inference",[22,23,24],"p",{},"Start by probing your GPU's VRAM to pick the optimal quantization: bf16 for 75+ GB, int8 for 40+ GB, int4 otherwise. This ensures the 35B MoE model (3B active params) fits without OOM errors. Install transformers>=4.48, accelerate, bitsandbytes, qwen-vl-utils, sentence-transformers. Prefer flash_attention_2 if available, fallback to sdpa.",[26,27,32],"pre",{"className":28,"code":29,"language":30,"meta":31,"style":31},"language-python shiki shiki-themes github-light github-dark","if VRAM_GB >= 75: LOAD_MODE = \"bf16\"\nelif VRAM_GB >= 40: LOAD_MODE = \"int8\"\nelse: LOAD_MODE = \"int4\"\nkwargs = dict(device_map=\"auto\", trust_remote_code=True, attn_implementation=ATTN_IMPL, torch_dtype=torch.bfloat16)\nif LOAD_MODE == \"int4\":\n    kwargs[\"quantization_config\"] = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)\nmodel = AutoModelForImageTextToText.from_pretrained(MODEL_ID, **kwargs)\n","python","",[33,34,35,43,49,55,61,67,73],"code",{"__ignoreMap":31},[36,37,40],"span",{"class":38,"line":39},"line",1,[36,41,42],{},"if VRAM_GB >= 75: LOAD_MODE = \"bf16\"\n",[36,44,46],{"class":38,"line":45},2,[36,47,48],{},"elif VRAM_GB >= 40: LOAD_MODE = \"int8\"\n",[36,50,52],{"class":38,"line":51},3,[36,53,54],{},"else: LOAD_MODE = \"int4\"\n",[36,56,58],{"class":38,"line":57},4,[36,59,60],{},"kwargs = dict(device_map=\"auto\", trust_remote_code=True, attn_implementation=ATTN_IMPL, torch_dtype=torch.bfloat16)\n",[36,62,64],{"class":38,"line":63},5,[36,65,66],{},"if LOAD_MODE == \"int4\":\n",[36,68,70],{"class":38,"line":69},6,[36,71,72],{},"    kwargs[\"quantization_config\"] = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)\n",[36,74,76],{"class":38,"line":75},7,[36,77,78],{},"model = AutoModelForImageTextToText.from_pretrained(MODEL_ID, **kwargs)\n",[22,80,81],{},"Principle: Dynamic loading maximizes hardware utilization—int4 on L4 GPUs still delivers coherent multimodal output. Common mistake: Fixed quantization ignores VRAM variance, crashing on smaller GPUs. Test on A100: ~70GB download, loads in ~60s, uses 20-40GB VRAM depending on mode.",[22,83,84,85,88,89,92,93,96],{},"Sampling presets tune behavior: ",[33,86,87],{},"thinking_general"," (temp=1.0, top_p=0.95) for open reasoning, ",[33,90,91],{},"thinking_coding"," (temp=0.6) for precise code. Thinking tags ",[33,94,95],{},"\u003Cthink>...\u003C\u002Fthink>"," enable inspectable chain-of-thought.",[22,98,99],{},"\"GPU: A100 | VRAM: 80.0 GB | CUDA 12.1 | torch 2.4.0\" — auto-detects setup for reproducibility.",[17,101,103],{"id":102},"qwenchat-session-persistent-framework-with-thinking-split","QwenChat: Session-Persistent Framework with Thinking Split",[22,105,106,107,110,111,110,114,117,118,121,122,125],{},"Core class manages history, tools, chat templates. Methods: ",[33,108,109],{},"user(content)",", ",[33,112,113],{},"assistant(content, reasoning)",[33,115,116],{},"tool_result(name, result)",". ",[33,119,120],{},"_inputs()"," applies template with ",[33,123,124],{},"enable_thinking=True"," for reasoning blocks.",[22,127,128,131,132,135,136,139,140,143,144,147,148,151,152,155],{},[33,129,130],{},"generate()"," produces think\u002Fans split: ",[33,133,134],{},"split_thinking(raw)"," extracts ",[33,137,138],{},"\u003Cthink>"," content. Appends to history automatically. ",[33,141,142],{},"stream()"," threads generation, callbacks ",[33,145,146],{},"on_thinking","\u002F",[33,149,150],{},"on_answer"," for real-time UI: buffers until ",[33,153,154],{},"\u003C\u002Fthink>",", then switches.",[26,157,159],{"className":28,"code":158,"language":30,"meta":31,"style":31},"def split_thinking(text: str):\n    if THINK_OPEN in text and THINK_CLOSE in text:\n        a = text.index(THINK_OPEN) + len(THINK_OPEN)\n        b = text.index(THINK_CLOSE)\n        return text[a:b].strip(), text[b + len(THINK_CLOSE):].strip()\n    return \"\", text.strip()\n",[33,160,161,166,171,176,181,186],{"__ignoreMap":31},[36,162,163],{"class":38,"line":39},[36,164,165],{},"def split_thinking(text: str):\n",[36,167,168],{"class":38,"line":45},[36,169,170],{},"    if THINK_OPEN in text and THINK_CLOSE in text:\n",[36,172,173],{"class":38,"line":51},[36,174,175],{},"        a = text.index(THINK_OPEN) + len(THINK_OPEN)\n",[36,177,178],{"class":38,"line":57},[36,179,180],{},"        b = text.index(THINK_CLOSE)\n",[36,182,183],{"class":38,"line":63},[36,184,185],{},"        return text[a:b].strip(), text[b + len(THINK_CLOSE):].strip()\n",[36,187,188],{"class":38,"line":69},[36,189,190],{},"    return \"\", text.strip()\n",[22,192,193,194,197],{},"Save\u002Fload JSON for persistence. Principle: Separate reasoning from answers prevents dilution in multi-turn chats; ",[33,195,196],{},"preserve_thinking=True"," carries prior thinks forward for agents. Mistake: Ignoring split leads to garbled streams—always parse tags.",[22,199,200,201,203],{},"Quality criteria: Thinking ~100-200 tokens for complex tasks; answers concise post-",[33,202,154],{},".",[22,205,206],{},"\"Loaded in 62s | VRAM used: 28.4 GB\" — efficient even quantized.",[17,208,210],{"id":209},"thinking-budget-tool-agents-and-structured-outputs","Thinking Budget, Tool Agents, and Structured Outputs",[22,212,213,216,217,219,220,222],{},[33,214,215],{},"ThinkingBudget(StoppingCriteria)"," caps reasoning tokens post-",[33,218,138],{},": stops if exceeds budget before ",[33,221,154],{},". Example: Frog well puzzle, budget=150 tokens—model simulates days without endless loops.",[22,224,225,226,229,230,233],{},"Agent loop: ",[33,227,228],{},"run_agent(user_msg, max_steps=5)","—generate with tools, parse ",[33,231,232],{},"\u003Ctool_call>{json}\u003C\u002Ftool_call>",", execute (calc, search_docs, get_time), feed results back. Schema-defined tools enable function calling.",[26,235,237],{"className":28,"code":236,"language":30,"meta":31,"style":31},"TOOL_CALL_RE = re.compile(r\"\u003Ctool_call>\\s*(\\{.*?\\})\\s*\u003C\u002Ftool_call>\", re.S)\nTOOLS_SCHEMA = [{\"type\":\"function\", \"function\":{\"name\":\"calculate\", ...}}]\n",[33,238,239,244],{"__ignoreMap":31},[36,240,241],{"class":38,"line":39},[36,242,243],{},"TOOL_CALL_RE = re.compile(r\"\u003Ctool_call>\\s*(\\{.*?\\})\\s*\u003C\u002Ftool_call>\", re.S)\n",[36,245,246],{"class":38,"line":45},[36,247,248],{},"TOOLS_SCHEMA = [{\"type\":\"function\", \"function\":{\"name\":\"calculate\", ...}}]\n",[22,250,251,252,255],{},"JSON extraction: ",[33,253,254],{},"json_with_retry(prompt, schema)"," strips fences, parses balanced {}, validates jsonschema, retries up to 3x with feedback. Handles malformed output reliably.",[22,257,258,259],{},"\"You reply with ONLY a single JSON object matching the user's schema. No markdown fences. No ",[260,261,262],"think",{}," blocks.\" — strict system prompt ensures purity.",[22,264,265],{},"Principle: Budgets prevent verbose reasoning explosion; retries fix hallucinated JSON. For Inception movie: 1-2 tries yields valid {\"title\":\"Inception\",\"year\":2010,...}.",[17,267,269],{"id":268},"moe-inspection-benchmarks-rag-and-vision-handling","MoE Inspection, Benchmarks, RAG, and Vision Handling",[22,271,272],{},"Hook routers (256 experts, top-8 + shared): Forward hooks count activations per token. Short prompt activates ~50 distinct experts unevenly—reveals routing dynamics.",[22,274,275],{},"Benchmark: Batch 1-4, measures tok\u002Fs (e.g., 150+ tok\u002Fs batch=1), VRAM peak. Resets cache between runs.",[22,277,278,279,282],{},"Mini-RAG: SentenceTransformer('all-MiniLM-L6-v2') embeds KB facts, cosine top-k retrieve. ",[33,280,281],{},"rag_answer()"," injects context, instructs grounded response.",[22,284,285,286,289],{},"Vision: Multimodal content=",[36,287,288],{},"{\"type\":\"image\",\"image\":\"url\"}, {\"type\":\"text\",\"text\":\"prompt\"}",". Handles math figures, object grounding (bbox JSON).",[22,291,292,293,296],{},"YaRN override for 1M context: Config ",[33,294,295],{},"rope_type:\"yarn\"",", factor=4.0—reload model with it.",[26,298,300],{"className":28,"code":299,"language":30,"meta":31,"style":31},"c.history.append({\"role\":\"user\",\"content\":[\n    {\"type\":\"image\",\"image\":IMG},\n    {\"type\":\"text\",\"text\":\"Locate every distinct object...\"}\n]})\n",[33,301,302,307,312,317],{"__ignoreMap":31},[36,303,304],{"class":38,"line":39},[36,305,306],{},"c.history.append({\"role\":\"user\",\"content\":[\n",[36,308,309],{"class":38,"line":45},[36,310,311],{},"    {\"type\":\"image\",\"image\":IMG},\n",[36,313,314],{"class":38,"line":51},[36,315,316],{},"    {\"type\":\"text\",\"text\":\"Locate every distinct object...\"}\n",[36,318,319],{"class":38,"line":57},[36,320,321],{},"]})\n",[22,323,324],{},"Principle: Inspect MoE for debugging (e.g., expert skew); RAG uses lightweight embeds over heavy rerankers for prototyping. Mistake: No padding_side='left' in batching slows inference.",[22,326,327],{},"\"distinct experts activated: 48\" — hands-on routing visibility.",[17,329,331],{"id":330},"key-takeaways","Key Takeaways",[333,334,335,339,345,352,355,358,361,364,367,370],"ul",{},[336,337,338],"li",{},"Probe VRAM and auto-quantize: int4 for \u003C40GB GPUs keeps multimodal coherent.",[336,340,341,342,344],{},"Use ",[33,343,95],{}," + split for inspectable reasoning; stream separately for UIs.",[336,346,347,348,351],{},"Implement ",[33,349,350],{},"ThinkingBudget"," to cap tokens—avoids infinite loops in puzzles\u002Fagents.",[336,353,354],{},"Agent loop: Parse tool_calls regex, execute schema tools, max_steps=5 prevents drift.",[336,356,357],{},"JSON retry + jsonschema: Reliable structured output even from creative models.",[336,359,360],{},"Hook MoE gates to count expert fires—debug routing imbalances.",[336,362,363],{},"Mini-RAG with MiniLM: Embed KB, cosine retrieve top-3 for grounded answers.",[336,365,366],{},"Vision prompts: List content with image URLs—native without extra VL utils.",[336,368,369],{},"Benchmark batches with left-padding: Quantify tok\u002Fs before scaling.",[336,371,372],{},"Persist sessions via JSON: Enables long-running prototypes across restarts.",[374,375,376],"style",{},"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":31,"searchDepth":45,"depth":45,"links":378},[379,380,381,382,383],{"id":19,"depth":45,"text":20},{"id":102,"depth":45,"text":103},{"id":209,"depth":45,"text":210},{"id":268,"depth":45,"text":269},{"id":330,"depth":45,"text":331},[385],"AI & LLMs",null,"md",false,{"content_references":390,"triage":398},[391,396],{"type":392,"title":393,"url":394,"context":395},"tool","Qwen\u002FQwen3.6-35B-A3B","https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen3.6-35B-A3B","mentioned",{"type":392,"title":397,"context":395},"sentence-transformers\u002Fall-MiniLM-L6-v2",{"relevance":63,"novelty":57,"quality":57,"actionability":63,"composite":399,"reasoning":400},4.55,"Category: AI & LLMs. The article provides a detailed tutorial on building a multimodal AI agent using Qwen 3.6, addressing practical implementation aspects like GPU-adaptive loading and session persistence, which are crucial for developers looking to integrate AI features into their products.",true,"\u002Fsummaries\u002Fbuild-multimodal-qwen-3-6-agents-with-thinking-too-summary","2026-04-21 07:54:34","2026-04-21 15:26:53",{"title":5,"description":31},{"loc":402},"db132ad11f333186","MarkTechPost","article","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F21\u002Fa-coding-implementation-on-qwen-3-6-35b-a3b-covering-multimodal-inference-thinking-control-tool-calling-moe-routing-rag-and-session-persistence\u002F","summaries\u002Fbuild-multimodal-qwen-3-6-agents-with-thinking-too-summary",[413,414,30,415],"llm","agents","ai-automation","Tutorial codes a full Qwen 3.6-35B-A3B framework: adaptive loading, thinking control, streaming, vision, agents, RAG, MoE inspection—ready for production prototyping on Colab 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LLM Agent: Skills, Registry, Dynamic Routing",{"provider":7,"model":8,"input_tokens":4829,"output_tokens":4830,"processing_time_ms":4831,"cost_usd":4832},8978,2516,27296,0.00303095,{"type":14,"value":4834,"toc":5489},[4835,4839,4862,4876,4887,4890,5025,5034,5041,5045,5054,5084,5094,5099,5114,5124,5127,5131,5146,5153,5162,5176,5186,5201,5204,5207,5211,5217,5241,5271,5274,5278,5288,5313,5348,5351,5357,5379,5382,5385,5388,5392,5405,5416,5422,5425,5428,5439,5442,5444,5487],[17,4836,4838],{"id":4837},"skill-abstractions-for-modular-capabilities","Skill Abstractions for Modular Capabilities",[22,4840,4841,4842,4845,4846,4849,4850,4853,4854,4857,4858,4861],{},"Skills are the core building blocks, modeled as self-describing, versioned modules analogous to OS syscalls. Each inherits from an abstract ",[33,4843,4844],{},"Skill"," base class requiring three methods: ",[33,4847,4848],{},"_define_metadata()"," for ",[33,4851,4852],{},"SkillMetadata"," (name, description, category, tags, dependencies, etc.), ",[33,4855,4856],{},"_define_schema()"," for OpenAI tool parameters (JSON schema), and ",[33,4859,4860],{},"execute(**kwargs)"," for implementation.",[22,4863,4864,4865,4868,4869,4872,4873,203],{},"Metadata uses ",[33,4866,4867],{},"@dataclass"," with ",[33,4870,4871],{},"SkillCategory"," enum (DATA, REASONING, etc.) for categorization. Execution tracks stats like call count and latency. Skills convert to OpenAI tools via ",[33,4874,4875],{},"to_openai_tool()",[22,4877,4878,4882,4883,4886],{},[4879,4880,4881],"strong",{},"Principle:"," Encapsulate logic with rich introspection—skills declare dependencies (",[33,4884,4885],{},"requires_skills",") and costs, enabling runtime validation and optimization.",[22,4888,4889],{},"Example: Calculator skill safely evaluates math expressions:",[26,4891,4893],{"className":28,"code":4892,"language":30,"meta":31,"style":31},"class CalculatorSkill(Skill):\n    def _define_metadata(self):\n        return SkillMetadata(\n            name=\"calculator\",\n            description=\"Evaluate mathematical expressions...\",\n            category=SkillCategory.REASONING,\n            tags=[\"math\", \"arithmetic\"],\n        )\n\n    def _define_schema(self):\n        return {\n            \"type\": \"object\",\n            \"properties\": {\"expression\": {\"type\": \"string\"}},\n            \"required\": [\"expression\"]\n        }\n\n    def execute(self, expression: str) -> str:\n        import math\n        safe = {\"__builtins__\": {}, \"sqrt\": math.sqrt, ...}  # Sandboxed eval\n        try:\n            return f\"Result: {eval(expression, safe)}\"\n        except Exception as ex:\n            return f\"Error: {ex}\"\n",[33,4894,4895,4900,4905,4910,4915,4920,4925,4930,4936,4942,4948,4954,4960,4966,4972,4978,4983,4989,4995,5001,5007,5013,5019],{"__ignoreMap":31},[36,4896,4897],{"class":38,"line":39},[36,4898,4899],{},"class CalculatorSkill(Skill):\n",[36,4901,4902],{"class":38,"line":45},[36,4903,4904],{},"    def _define_metadata(self):\n",[36,4906,4907],{"class":38,"line":51},[36,4908,4909],{},"        return SkillMetadata(\n",[36,4911,4912],{"class":38,"line":57},[36,4913,4914],{},"            name=\"calculator\",\n",[36,4916,4917],{"class":38,"line":63},[36,4918,4919],{},"            description=\"Evaluate mathematical expressions...\",\n",[36,4921,4922],{"class":38,"line":69},[36,4923,4924],{},"            category=SkillCategory.REASONING,\n",[36,4926,4927],{"class":38,"line":75},[36,4928,4929],{},"            tags=[\"math\", \"arithmetic\"],\n",[36,4931,4933],{"class":38,"line":4932},8,[36,4934,4935],{},"        )\n",[36,4937,4939],{"class":38,"line":4938},9,[36,4940,4941],{"emptyLinePlaceholder":401},"\n",[36,4943,4945],{"class":38,"line":4944},10,[36,4946,4947],{},"    def _define_schema(self):\n",[36,4949,4951],{"class":38,"line":4950},11,[36,4952,4953],{},"        return {\n",[36,4955,4957],{"class":38,"line":4956},12,[36,4958,4959],{},"            \"type\": \"object\",\n",[36,4961,4963],{"class":38,"line":4962},13,[36,4964,4965],{},"            \"properties\": {\"expression\": {\"type\": \"string\"}},\n",[36,4967,4969],{"class":38,"line":4968},14,[36,4970,4971],{},"            \"required\": [\"expression\"]\n",[36,4973,4975],{"class":38,"line":4974},15,[36,4976,4977],{},"        }\n",[36,4979,4981],{"class":38,"line":4980},16,[36,4982,4941],{"emptyLinePlaceholder":401},[36,4984,4986],{"class":38,"line":4985},17,[36,4987,4988],{},"    def execute(self, expression: str) -> str:\n",[36,4990,4992],{"class":38,"line":4991},18,[36,4993,4994],{},"        import math\n",[36,4996,4998],{"class":38,"line":4997},19,[36,4999,5000],{},"        safe = {\"__builtins__\": {}, \"sqrt\": math.sqrt, ...}  # Sandboxed eval\n",[36,5002,5004],{"class":38,"line":5003},20,[36,5005,5006],{},"        try:\n",[36,5008,5010],{"class":38,"line":5009},21,[36,5011,5012],{},"            return f\"Result: {eval(expression, safe)}\"\n",[36,5014,5016],{"class":38,"line":5015},22,[36,5017,5018],{},"        except Exception as ex:\n",[36,5020,5022],{"class":38,"line":5021},23,[36,5023,5024],{},"            return f\"Error: {ex}\"\n",[22,5026,5027,5028,4849,5031,203],{},"This prevents injection attacks via restricted globals. Output: ",[33,5029,5030],{},"Result: 1024",[33,5032,5033],{},"'2**10'",[22,5035,5036,5037,5040],{},"Common pitfall: Unrestricted ",[33,5038,5039],{},"eval","—always sandbox. Quality criteria: Schema must match LLM expectations; metadata descriptions guide tool selection precisely.",[17,5042,5044],{"id":5043},"central-registry-for-dynamic-discovery","Central Registry for Dynamic Discovery",[22,5046,5047,5050,5051,203],{},[33,5048,5049],{},"SkillRegistry"," acts as a catalog: register skills by name, index by category\u002Ftags, list\u002Ffilter, and expose as OpenAI tools. Supports hot-loading via ",[33,5052,5053],{},"SkillLoader",[26,5055,5057],{"className":28,"code":5056,"language":30,"meta":31,"style":31},"registry = SkillRegistry()\nregistry.register(CalculatorSkill())\nregistry.register(TextSummarizerSkill())\n# ...\nconsole.print(registry.display())  # Rich table view\n",[33,5058,5059,5064,5069,5074,5079],{"__ignoreMap":31},[36,5060,5061],{"class":38,"line":39},[36,5062,5063],{},"registry = SkillRegistry()\n",[36,5065,5066],{"class":38,"line":45},[36,5067,5068],{},"registry.register(CalculatorSkill())\n",[36,5070,5071],{"class":38,"line":51},[36,5072,5073],{},"registry.register(TextSummarizerSkill())\n",[36,5075,5076],{"class":38,"line":57},[36,5077,5078],{},"# ...\n",[36,5080,5081],{"class":38,"line":63},[36,5082,5083],{},"console.print(registry.display())  # Rich table view\n",[22,5085,5086,5087,110,5090,5093],{},"Registry methods: ",[33,5088,5089],{},"get_by_category()",[33,5091,5092],{},"to_openai_tools(names=None)"," filters tools dynamically. Principle: Decouple skill definition from invocation—LLM sees only relevant tools.",[22,5095,5096],{},[4879,5097,5098],{},"Hot-loading example:",[26,5100,5102],{"className":28,"code":5101,"language":30,"meta":31,"style":31},"loader = SkillLoader(registry)\nloader.load(FactCheckerSkill)  # Registers instantly\n",[33,5103,5104,5109],{"__ignoreMap":31},[36,5105,5106],{"class":38,"line":39},[36,5107,5108],{},"loader = SkillLoader(registry)\n",[36,5110,5111],{"class":38,"line":45},[36,5112,5113],{},"loader.load(FactCheckerSkill)  # Registers instantly\n",[22,5115,5116,5117,5120,5121,203],{},"Unload with ",[33,5118,5119],{},"loader.unload('name')",". Enables runtime extensibility without restarts. Avoid overloading LLM with all tools—filter by context or query ",[33,5122,5123],{},"skill_introspector",[22,5125,5126],{},"\"Central catalog of all agent capabilities. Analogue: OS process\u002Fsyscall table.\"",[17,5128,5130],{"id":5129},"implementing-specialized-skills","Implementing Specialized Skills",[22,5132,5133,5134,5137,5138,5141,5142,5145],{},"Extend for NLP\u002Freasoning: ",[33,5135,5136],{},"TextSummarizerSkill"," uses LLM with mode-specific prompts (brief\u002Fstandard\u002Fdetailed). ",[33,5139,5140],{},"DataAnalystSkill"," ingests JSON\u002FCSV, answers questions. ",[33,5143,5144],{},"CodeGeneratorSkill"," outputs commented Python.",[22,5147,5148,5149,5152],{},"JSON-structured outputs for parseability, e.g., ",[33,5150,5151],{},"FactCheckerSkill",":",[26,5154,5156],{"className":28,"code":5155,"language":30,"meta":31,"style":31},"{\"verdict\":\"true|false|uncertain\",\"confidence\":0.7,\"explanation\":\"...\"}\n",[33,5157,5158],{"__ignoreMap":31},[36,5159,5160],{"class":38,"line":39},[36,5161,5155],{},[22,5163,5164,5167,5168,5171,5172,5175],{},[33,5165,5166],{},"SentimentAnalyzerSkill"," adds emotion scores optionally. ",[33,5169,5170],{},"TranslationSkill"," controls formality. All leverage ",[33,5173,5174],{},"gpt-4o-mini"," for cost-efficiency.",[22,5177,5178,5181,5182,5185],{},[4879,5179,5180],{},"Meta-skill:"," ",[33,5183,5184],{},"SkillIntrospectorSkill(registry)"," lists\u002Fdescribes skills:",[333,5187,5188,5195],{},[336,5189,5190,5191,5194],{},"Action ",[33,5192,5193],{},"list",": Bullet list of skills.",[336,5196,5197,5200],{},[33,5198,5199],{},"describe",": Full metadata\u002Fschema.",[22,5202,5203],{},"Principle: Self-awareness prevents hallucinated tool calls. Prompt LLM to use introspector when unsure: \"Use skill_introspector if unsure which skill to pick.\"",[22,5205,5206],{},"Pitfall: Vague descriptions lead to wrong routing—be precise, e.g., \"Assess factual accuracy... Returns verdict, confidence...\"",[17,5208,5210],{"id":5209},"composite-skills-and-orchestration","Composite Skills and Orchestration",[22,5212,5213,5216],{},[33,5214,5215],{},"ResearchReportSkill(registry)"," composes sub-skills fractally:",[5218,5219,5220,5227,5234],"ol",{},[336,5221,5222,5223,5226],{},"Summarize data (",[33,5224,5225],{},"text_summarizer",", detailed mode).",[336,5228,5229,5230,5233],{},"Analyze quantitatively (",[33,5231,5232],{},"data_analyst",").",[336,5235,5236,5237,5240],{},"Generate visualization code (",[33,5238,5239],{},"code_generator",", optional).",[26,5242,5244],{"className":28,"code":5243,"language":30,"meta":31,"style":31},"def execute(self, topic: str, data: str, include_code: bool = True) -> str:\n    summary = self._registry.get(\"text_summarizer\")(text=data, mode=\"detailed\")\n    analysis = self._registry.get(\"data_analyst\")(data=data, question=f\"Key insights about {topic}\")\n    # ...\n    return markdown_report\n",[33,5245,5246,5251,5256,5261,5266],{"__ignoreMap":31},[36,5247,5248],{"class":38,"line":39},[36,5249,5250],{},"def execute(self, topic: str, data: str, include_code: bool = True) -> str:\n",[36,5252,5253],{"class":38,"line":45},[36,5254,5255],{},"    summary = self._registry.get(\"text_summarizer\")(text=data, mode=\"detailed\")\n",[36,5257,5258],{"class":38,"line":51},[36,5259,5260],{},"    analysis = self._registry.get(\"data_analyst\")(data=data, question=f\"Key insights about {topic}\")\n",[36,5262,5263],{"class":38,"line":57},[36,5264,5265],{},"    # ...\n",[36,5267,5268],{"class":38,"line":63},[36,5269,5270],{},"    return markdown_report\n",[22,5272,5273],{},"Logs sub-calls for observability. Dependencies declared in metadata validate composition.",[17,5275,5277],{"id":5276},"agent-execution-loop-with-tool-routing","Agent Execution Loop with Tool Routing",[22,5279,5280,5283,5284,5287],{},[33,5281,5282],{},"SkillBasedAgent"," orchestrates via ReAct-like loop (up to ",[33,5285,5286],{},"max_iterations=6","):",[5218,5289,5290,5293,5299,5306],{},[336,5291,5292],{},"System prompt lists principles and loaded skills.",[336,5294,5295,5296,203],{},"LLM gets tools from registry, calls via ",[33,5297,5298],{},"tool_choice=\"auto\"",[336,5300,5301,5302,5305],{},"Dispatch: ",[33,5303,5304],{},"registry.get(name)(**args)",", append tool result to messages.",[336,5307,5308,5309,5312],{},"Repeat until ",[33,5310,5311],{},"finish_reason=\"stop\""," or max iterations.",[26,5314,5316],{"className":28,"code":5315,"language":30,"meta":31,"style":31},"def run(self, user_input: str) -> str:\n    messages = [{\"role\": \"system\", \"content\": self.system_prompt}, {\"role\": \"user\", \"content\": user_input}]\n    for i in range(self.max_iterations):\n        resp = client.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n        # Handle tool_calls, dispatch, append results\n    return final_answer\n",[33,5317,5318,5323,5328,5333,5338,5343],{"__ignoreMap":31},[36,5319,5320],{"class":38,"line":39},[36,5321,5322],{},"def run(self, user_input: str) -> str:\n",[36,5324,5325],{"class":38,"line":45},[36,5326,5327],{},"    messages = [{\"role\": \"system\", \"content\": self.system_prompt}, {\"role\": \"user\", \"content\": user_input}]\n",[36,5329,5330],{"class":38,"line":51},[36,5331,5332],{},"    for i in range(self.max_iterations):\n",[36,5334,5335],{"class":38,"line":57},[36,5336,5337],{},"        resp = client.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",[36,5339,5340],{"class":38,"line":63},[36,5341,5342],{},"        # Handle tool_calls, dispatch, append results\n",[36,5344,5345],{"class":38,"line":69},[36,5346,5347],{},"    return final_answer\n",[22,5349,5350],{},"Verbose mode uses Rich panels\u002Ftables for traces. Synthesizes multi-tool outputs into coherent response.",[22,5352,5353,5356],{},[4879,5354,5355],{},"Example workflow:"," User: \"Summarize this sales data and check if growth claim is true.\"",[333,5358,5359,5365,5370,5376],{},[336,5360,5361,5362,5364],{},"Calls ",[33,5363,5225],{}," → summary.",[336,5366,5367,5369],{},[33,5368,5232],{}," → insights.",[336,5371,5372,5375],{},[33,5373,5374],{},"fact_checker"," → verdict.",[336,5377,5378],{},"Final: Integrated report.",[22,5380,5381],{},"Principle: LLM routes dynamically—no hardcoded if\u002Felse. Trade-off: Token cost scales with iterations\u002Ftools; mitigate with targeted tools and cheap model.",[22,5383,5384],{},"\"PRINCIPLES: 1. Use the most appropriate skill... 2. Chain multiple skills... 3. Use skill_introspector... 4. Synthesize...\"",[22,5386,5387],{},"Pitfall: Infinite loops—cap iterations, clear tool results properly. Quality: Final answer must weave tool outputs, not dump raw.",[17,5389,5391],{"id":5390},"runtime-extensibility-and-observability","Runtime Extensibility and Observability",[22,5393,5394,5396,5397,5400,5401,5404],{},[33,5395,5053],{}," mirrors package managers: ",[33,5398,5399],{},"load(skill_class, *args)"," instantiates\u002Fregisters. Supports registry-dependent skills (e.g., pass ",[33,5402,5403],{},"registry"," to composites).",[22,5406,5407,5408,5411,5412,5415],{},"Stats via ",[33,5409,5410],{},"skill.stats",": ",[33,5413,5414],{},"{\"calls\": 5, \"avg_latency_ms\": 120}",". Display registry table shows usage at glance.",[22,5417,5418,5421],{},[4879,5419,5420],{},"Dashboard-like:"," Rich tables for skills, iteration traces. Extend with LangSmith\u002FPhoenix for production.",[22,5423,5424],{},"\"Hot-loaded skill: research_report\"—no restart needed.",[22,5426,5427],{},"Assumes: Python proficiency, OpenAI tool calling basics. Fits after simple function calling, before full agent frameworks like LangGraph.",[22,5429,5430,5431,5434,5435,5438],{},"Practice: Add ",[33,5432,5433],{},"WebSearchSkill"," (requires API), compose into ",[33,5436,5437],{},"MarketResearchSkill",". Test chaining: math → plot code → sentiment on results.",[22,5440,5441],{},"\"Each Skill is: self-describing · versioned · testable · composable.\"",[17,5443,331],{"id":330},[333,5445,5446,5449,5454,5457,5460,5466,5469,5472,5478,5484],{},[336,5447,5448],{},"Define skills with metadata\u002Fschema\u002Fexecute for LLM compatibility and introspection.",[336,5450,341,5451,5453],{},[33,5452,5049],{}," to index and expose tools dynamically—filter to avoid context overflow.",[336,5455,5456],{},"Implement agent loop: LLM reasons → tool call → dispatch → synthesize, max 6 iterations.",[336,5458,5459],{},"Compose skills hierarchically; declare dependencies for validation.",[336,5461,5462,5463,5465],{},"Hot-load via ",[33,5464,5053],{}," for extensibility; track stats for optimization.",[336,5467,5468],{},"Sandbox executions (e.g., safe eval); structure outputs as JSON for parsing.",[336,5470,5471],{},"Prompt with principles: appropriate skill, chain, introspect, synthesize.",[336,5473,5474,5475,5477],{},"Start with ",[33,5476,5174],{}," for cost; upgrade for complex reasoning.",[336,5479,5480,5481,5483],{},"Add ",[33,5482,5123],{}," always—enables discovery without prompt bloat.",[336,5485,5486],{},"Observe via console traces; productionize with external logging.",[374,5488,376],{},{"title":31,"searchDepth":45,"depth":45,"links":5490},[5491,5492,5493,5494,5495,5496,5497],{"id":4837,"depth":45,"text":4838},{"id":5043,"depth":45,"text":5044},{"id":5129,"depth":45,"text":5130},{"id":5209,"depth":45,"text":5210},{"id":5276,"depth":45,"text":5277},{"id":5390,"depth":45,"text":5391},{"id":330,"depth":45,"text":331},[385],{"content_references":5500,"triage":5501},[],{"relevance":63,"novelty":57,"quality":57,"actionability":63,"composite":399,"reasoning":5502},"Category: AI & LLMs. The article provides a detailed framework for building a modular skill-based agent system for LLMs, addressing the audience's need for practical applications in AI integration. It includes specific code examples and actionable steps for implementation, making it highly relevant and immediately applicable.","\u002Fsummaries\u002Fmodular-llm-agent-skills-registry-dynamic-routing-summary","2026-05-05 20:47:25","2026-05-06 16:14:16",{"title":4827,"description":31},{"loc":5503},"795472d520b82a5d","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F05\u002Fbuild-a-modular-skill-based-agent-system-for-llms-with-dynamic-tool-routing-in-python\u002F","summaries\u002Fmodular-llm-agent-skills-registry-dynamic-routing-summary",[414,30,413,415],"Build a Python agent system where LLMs dynamically select and chain modular skills via a central registry, enabling composable workflows, hot-loading, and multi-step reasoning.",[415],"_cxNjxqyKcFDUN321-4rm1we7-s7USeiQI_MVY8Nxaw",{"id":5516,"title":5517,"ai":5518,"body":5523,"categories":5597,"created_at":386,"date_modified":386,"description":31,"extension":387,"faq":386,"featured":388,"kicker_label":386,"meta":5598,"navigation":401,"path":5608,"published_at":5609,"question":386,"scraped_at":5610,"seo":5611,"sitemap":5612,"source_id":5613,"source_name":408,"source_type":409,"source_url":5614,"stem":5615,"tags":5616,"thumbnail_url":386,"tldr":5617,"tweet":386,"unknown_tags":5618,"__hash__":5619},"summaries\u002Fsummaries\u002Fmulti-agent-ai-pipeline-for-systems-biology-analys-summary.md","Multi-Agent AI Pipeline for Systems Biology Analysis",{"provider":7,"model":8,"input_tokens":5519,"output_tokens":5520,"processing_time_ms":5521,"cost_usd":5522},9819,2641,25076,0.00326165,{"type":14,"value":5524,"toc":5592},[5525,5529,5549,5557,5561,5576,5579,5583,5586,5589],[17,5526,5528],{"id":5527},"synthetic-data-foundations-enable-modular-analysis","Synthetic Data Foundations Enable Modular Analysis",[22,5530,5531,5532,5536,5537,5540,5541,5544,5545,5548],{},"Generate structured inputs for four biological layers using fixed parameters for reproducibility. For gene regulatory networks, create a 14x14 weight matrix W with edge_prob=0.20 (uniform -1.5 to 1.5 weights, excluding self-loops), simulate 80-step expression X via X_t = sigmoid(X_ @ W + N(0,0.08)). Protein features (40 proteins, 10D normals) include families (5 classes) and localization (4 classes); PPI dataset from 780 pairs uses cosine sim, family\u002Flocal same flags, abs diff\u002Felementwise product feats, latent score=1.4",[5533,5534,5535],"em",{},"sim +1.0","fam_same +0.8",[5533,5538,5539],{},"loc_same +0.15","hidden_proj yielding sigmoid prob labels. Metabolic net has 7 reactions\u002Fmetabolites (e.g., R3_TCA: 1.0 biomass, 2.4 ATP yield, 1.4 O2 need) with substrate_costs. Cell signaling ODEs (T=220, dt=0.05, ligand=1.2) model receptor\u002Fkinase\u002FTF\u002Fphosphatase dynamics via rates like dR=1.6",[5533,5542,5543],{},"lig","(1-R)-0.9*R, clipping ",[36,5546,5547],{},"0,1"," (phos to 1.5).",[22,5550,5551,5552,5556],{},"These functions produce analyzable outputs: GRN yields ~20-30 true edges; PPI ~10-15% positives; met balances biomass\u002FATP vs constraints; signaling reaches peaks (e.g., receptor ",[5553,5554,5555],"del",{},"0.8 at t","10-20).",[17,5558,5560],{"id":5559},"specialized-agents-extract-key-metrics-and-rankings","Specialized Agents Extract Key Metrics and Rankings",[22,5562,5563,5564,5567,5568,5571,5572,5575],{},"GeneRegulatoryNetworkAgent infers edges from |corrcoef(X.T)|>0.35 (yielding ~15-25 associations vs true edges), builds DiGraph for top-5 hubs\u002Fsinks by out\u002Fin-degree (e.g., G5 out_deg=4), ranks most_dynamic by var(X",[36,5565,5566],{},":,g",") (top often >0.05). ProteinInteractionPredictionAgent splits PPI rows, scales feats, fits LogisticRegression(max_iter=1000), reports test ROC-AUC\u002FAP (~0.85-0.90 on held-out), ranks top-10 pairs by pred_prob (e.g., P12-P28:0.92). MetabolicOptimizationAgent runs 8000 random Dirichlet(ones(6))",[5533,5569,5570],{},"U(1.5,5) fluxes, penalizes O2>3.5\u002Fsub>4.2 by 6","(excess), scores 2.2",[5533,5573,5574],{},"biomass+0.6","ATP (best ~5-7, e.g., R3_TCA flux=2.1 dominant). CellSignalingSimulationAgent computes max\u002Fpeak_time for receptor\u002Fkinase\u002FTF (~0.75\u002F0.85\u002F0.65 at t=15\u002F25\u002F35), final states.",[22,5577,5578],{},"Agents return dict summaries with exact counts (e.g., 14 genes, 196 pairs, 0.124 pos rate), top lists, preserving floats rounded to 4dec for downstream use—enables quick ranking without retraining.",[17,5580,5582],{"id":5581},"workflow-integration-visualization-and-llm-synthesis","Workflow Integration, Visualization, and LLM Synthesis",[22,5584,5585],{},"Execute agents sequentially on generated data, aggregate AgentResult list, print JSON summaries\u002Ftables (e.g., dynamic genes G7 var=0.0824), plot weight matrices (imshow coolwarm), expression trajectories (6 lines), signaling curves (4 components), met trace (converging to best), networks (spring_layout, green\u002Fred edges >0.4 |W|, PPI widths=2+4*prob). Save artifact JSON.",[22,5587,5588],{},"PrincipalInvestigatorAgent prompts GPT-4o-mini (temp=0.4) with agent summaries to generate report: Executive Summary, Key Findings (per-agent), Cross-System Interpretation (e.g., dynamic hubs link to PPI clusters driving met flux\u002Fsignaling amplification), Wet-Lab Hypotheses, Limitations (synthetic data), Extensions (real omics). Prompt enforces concise science, no fabrication—yields coherent story tying regulation to metabolism\u002Fsignaling via interactions, runnable in Colab for rapid prototyping.",[22,5590,5591],{},"Trade-offs: Synthetic data ignores real priors (extend with omics); random met opt crude vs LP solvers; correlation inference misses causality (add Granger); scales to 100s genes\u002Fproteins but LLMs add latency\u002Fcost (~$0.01\u002Frun).",{"title":31,"searchDepth":45,"depth":45,"links":5593},[5594,5595,5596],{"id":5527,"depth":45,"text":5528},{"id":5559,"depth":45,"text":5560},{"id":5581,"depth":45,"text":5582},[385],{"content_references":5599,"triage":5605},[5600],{"type":5601,"title":5602,"url":5603,"context":5604},"other","Full Codes with Notebook","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fai_agents_biological_systems_modeling_Marktechpost(1).ipynb","recommended",{"relevance":51,"novelty":51,"quality":57,"actionability":45,"composite":5606,"reasoning":5607},3.05,"Category: AI Automation. The article discusses a multi-agent AI pipeline for biological analysis, which maps to AI automation but lacks direct applicability for product builders outside of the specific domain of systems biology. While it presents some novel approaches to generating synthetic data and modeling, the practical steps for implementation in a broader context are limited.","\u002Fsummaries\u002Fmulti-agent-ai-pipeline-for-systems-biology-analys-summary","2026-05-02 20:31:07","2026-05-03 17:01:46",{"title":5517,"description":31},{"loc":5608},"71ab353ce61b307b","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F02\u002Fbuild-a-multi-agent-ai-workflow-for-biological-network-modeling-protein-interactions-metabolism-and-cell-signaling-simulation\u002F","summaries\u002Fmulti-agent-ai-pipeline-for-systems-biology-analys-summary",[414,413,30,415],"Use Python agents to generate synthetic bio data for gene regulation (14 genes, 0.20 edge prob), predict PPIs (LR AUC\u002FAP on feature diffs\u002Fsims), optimize metabolism (8000 flux iters under O2\u002Fsubstrate budgets), simulate signaling (ODE peaks\u002Ftimings), then GPT-4o-mini synthesizes integrated report.",[415],"_Bdp8Eoje1MTIdlt1hEqA9rFu3d3Ie3wv3xKsNwt2pM",{"id":5621,"title":5622,"ai":5623,"body":5628,"categories":5746,"created_at":386,"date_modified":386,"description":31,"extension":387,"faq":386,"featured":388,"kicker_label":386,"meta":5747,"navigation":401,"path":5764,"published_at":5765,"question":386,"scraped_at":5766,"seo":5767,"sitemap":5768,"source_id":5769,"source_name":5770,"source_type":409,"source_url":5771,"stem":5772,"tags":5773,"thumbnail_url":386,"tldr":5774,"tweet":386,"unknown_tags":5775,"__hash__":5776},"summaries\u002Fsummaries\u002Frefactoring-a-sales-agent-to-production-with-adk-v-summary.md","Refactoring a Sales Agent to Production with ADK & Vectors",{"provider":7,"model":8,"input_tokens":5624,"output_tokens":5625,"processing_time_ms":5626,"cost_usd":5627},8773,2093,12240,0.0027786,{"type":14,"value":5629,"toc":5739},[5630,5634,5637,5640,5643,5647,5650,5653,5656,5659,5663,5673,5676,5679,5686,5690,5693,5696,5699,5701,5730,5733,5736],[17,5631,5633],{"id":5632},"from-weekend-hack-to-scalable-sales-tool","From Weekend Hack to Scalable Sales Tool",[22,5635,5636],{},"Jacob Battish, a non-technical Google executive, built \"Titanium\"—a sales agent that researches target companies, identifies executive pain points, and drafts personalized outreach emails tying them to Google's strengths. Coded in evenings and weekends using Gemini SDK, it parallelizes research via fan-out (processing multiple companies simultaneously), employs low-temperature prompts for factual grounding with Google Search, verifies intel to curb hallucinations, and includes exponential backoff for reliability. However, it relies on just 10-12 hardcoded case studies, limiting scalability for his team.",[22,5638,5639],{},"\"I vibe coded this in the evenings and weekends. I was blown away at how doable it was. If I can do it, anyone can do it,\" Jacob shared, highlighting how iterative prompting with Gemini taught him production techniques like parallelism, reducing runtime from 15 minutes.",[22,5641,5642],{},"Luis, the host, praised the structure: parallel sub-agents per company, grounded generation, and verification. But scalability demanded dynamic access to Google's 1,600+ public case studies.",[17,5644,5646],{"id":5645},"migrating-to-adk-for-robust-agent-workflows","Migrating to ADK for Robust Agent Workflows",[22,5648,5649],{},"To productionize, they ported to Google's Agent Development Kit (ADK), a specialized SDK for agentic flows. ADK replaces nested if-loops and manual orchestration with declarative constructs like sequential agents (step-by-step execution) and root agents orchestrating sub-tasks.",[22,5651,5652],{},"A coding agent (using ADK dev skills compatible with tools like Anti-Gravity and Gemini CLI) generated a plan: replicate v1 workflow, then enhance. Side-by-side, original Python used raw Gemini calls in loops; ADK expresses the same as modular agents: research → verify → generate email, with built-in error handling and maintainability.",[22,5654,5655],{},"\"When developing an agent, it is absolutely perfectly a legitimate tactic to use the native SDK... however, to set yourself up for success... shift over to a specialized SDK specifically designed for agents,\" Luis advised. Jacob's instinct: \"Create a plan to do this and... go step by step.\"",[22,5657,5658],{},"They preserved fan-out parallelism, added ENV-based GCP credentials, and iterated prompts like \"then reverify your work\" to catch misses.",[17,5660,5662],{"id":5661},"dynamic-case-study-retrieval-via-crawler-and-vector-search","Dynamic Case Study Retrieval via Crawler and Vector Search",[22,5664,5665,5666,5672],{},"Core upgrade: a Playwright-based crawler scrapes Google's case study site (",[5667,5668,5669],"a",{"href":5669,"rel":5670},"https:\u002F\u002Fcloud.google.com\u002Fcustomers",[5671],"nofollow","). Phase 1: Load pages, click \"show more\" repeatedly to extract ~1,600 URLs. Phase 2: Fetch HTML, use Gemini to extract\u002Freformat as Markdown JSON, chunk and embed into Google Vector Search 2.0.",[22,5674,5675],{},"Luis chose managed Vector Search over local options like Chroma DB or Pinecone for seamless GCP integration. Querying uses hybrid search: semantic + text, combined and reranked for relevance.",[22,5677,5678],{},"Agent tools expose vector search: input query (e.g., company pains), retrieve top matches, feed to email generation. Demo: Target a CTO—agent searches exec details, company priorities, vectors relevant cases, drafts punchy email like \"I know you're struggling with ABC. Google's really good at XYZ.\"",[22,5680,5681,5682,5685],{},"\"The agent developed a tool that goes to the website and it's actually manually clicking ",[36,5683,5684],{},"show more","... figuring out the links,\" Luis noted, showcasing autonomous tool use.",[17,5687,5689],{"id":5688},"production-polish-ui-reliability-and-trade-offs","Production Polish: UI, Reliability, and Trade-offs",[22,5691,5692],{},"Post-refactor: Simple web UI for company\u002Frole input, copy-paste outputs. Benefits over v1: Scalable (full case studies), robust (ADK workflows, hybrid search), team-ready. Trade-offs: Initial setup time (60-min live refactor hit snags like laptop freezes), but ADK accelerates iteration.",[22,5694,5695],{},"Q&A unpacked: Chunking via Gemini-structured JSON; hybrid search boosts recall; ADK simplifies orchestration vs. raw code. Jacob: \"Taking it from a tool that I'm using as really a pilot into now something that's much more production ready... incredible.\"",[22,5697,5698],{},"They committed to polish: Clean code, blog post, full repo. Chat feedback: Get to coding faster (Luis: \"I probably talked too much\").",[17,5700,331],{"id":330},[333,5702,5703,5706,5709,5712,5715,5718,5721,5724,5727],{},[336,5704,5705],{},"Start agents with native SDKs like Gemini for prototypes, migrate to ADK for production workflows—declarative agents beat nested loops.",[336,5707,5708],{},"Build reliability early: Fan-out parallelism, exponential backoff, low temperature, verification prompts, reverify steps.",[336,5710,5711],{},"For dynamic RAG, crawler + vector DB: Use Playwright for JS-heavy sites, Gemini for extraction, hybrid semantic\u002Ftext search.",[336,5713,5714],{},"Non-technical builders: Iterate via prompting—\"how can we improve this?\" yields exponential gains.",[336,5716,5717],{},"Expose agent tools clearly: Vector functions for retrieval, grounded search for facts.",[336,5719,5720],{},"Add UI last for usability; share repos\u002Fblogs for team adoption.",[336,5722,5723],{},"Plan before code: Coding agents excel at step-by-step porting.",[336,5725,5726],{},"Choose managed vectors (e.g., Google Vector Search) for scale over local for speed.",[336,5728,5729],{},"Hybrid search > single mode: Rerank results for relevance.",[22,5731,5732],{},"\"Something I've learned: I always now add in then reverify your work. Make it go back a second time cuz it catches things that it misses.\" – Jacob Battish",[22,5734,5735],{},"\"Structured messaging is the best way to connect one system to another.\" – Luis",[22,5737,5738],{},"\"This is awesome... much more confident talking about ADK... with my customers.\" – Jacob Battish",{"title":31,"searchDepth":45,"depth":45,"links":5740},[5741,5742,5743,5744,5745],{"id":5632,"depth":45,"text":5633},{"id":5645,"depth":45,"text":5646},{"id":5661,"depth":45,"text":5662},{"id":5688,"depth":45,"text":5689},{"id":330,"depth":45,"text":331},[385],{"content_references":5748,"triage":5761},[5749,5751,5753,5755,5757,5759],{"type":392,"title":5750,"context":5604},"Agent Development Kit (ADK)",{"type":392,"title":5752,"context":395},"Gemini SDK",{"type":392,"title":5754,"context":395},"Playwright",{"type":392,"title":5756,"context":395},"Google Vector Search 2.0",{"type":392,"title":5758,"context":395},"Chroma DB",{"type":392,"title":5760,"context":395},"Pinecone",{"relevance":63,"novelty":57,"quality":57,"actionability":57,"composite":5762,"reasoning":5763},4.35,"Category: AI Automation. The article provides a detailed account of refactoring a sales agent using Google's ADK, addressing practical applications of AI automation in production. It offers insights into specific techniques like parallelism and dynamic vector search, which are actionable for builders looking to enhance their AI tools.","\u002Fsummaries\u002Frefactoring-a-sales-agent-to-production-with-adk-v-summary","2026-04-15 16:48:15","2026-04-20 16:55:21",{"title":5622,"description":31},{"loc":5764},"6abaf25458827723","Google Cloud Tech","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=md2VFN6SojQ","summaries\u002Frefactoring-a-sales-agent-to-production-with-adk-v-summary",[414,30,413,415],"Non-technical builder Jacob's Gemini agent for sales outreach gets refactored live using Google's ADK: swaps hardcoded case studies for dynamic vector search over 1,600 Google cases, adds parallelism, reliability, and UI for team scalability.",[415],"sb6iVLULLAiHoD-G309spryfW-y4kfsUhhtUA9cfBzI",{"id":5778,"title":5779,"ai":5780,"body":5785,"categories":5813,"created_at":386,"date_modified":386,"description":31,"extension":387,"faq":386,"featured":388,"kicker_label":386,"meta":5814,"navigation":401,"path":5815,"published_at":5816,"question":386,"scraped_at":386,"seo":5817,"sitemap":5818,"source_id":5819,"source_name":5820,"source_type":409,"source_url":5821,"stem":5822,"tags":5823,"thumbnail_url":386,"tldr":5824,"tweet":386,"unknown_tags":5825,"__hash__":5826},"summaries\u002Fsummaries\u002Fmulti-agent-debate-unpacks-portfolio-drift-causes-summary.md","Multi-Agent Debate Unpacks Portfolio Drift Causes",{"provider":7,"model":8,"input_tokens":5781,"output_tokens":5782,"processing_time_ms":5783,"cost_usd":5784},7797,1501,10019,0.0018395,{"type":14,"value":5786,"toc":5808},[5787,5791,5794,5798,5801,5805],[17,5788,5790],{"id":5789},"multi-agent-discussion-exposes-causal-tensions-single-agents-miss","Multi-Agent Discussion Exposes Causal Tensions Single Agents Miss",[22,5792,5793],{},"Portfolio drift—e.g., allocations shifting from 60% equities\u002F30% fixed income\u002F10% alternatives to 68-72% equities despite rebalancing—defies single-threaded analysis because causes span stale holdings snapshots, optimizer sensitivity to partial inputs, partial fills in illiquid names, resized trades from risk checks, and delayed post-trade reconciliation. A solo agent produces a smooth narrative hiding assumption conflicts; instead, assign independent voices to DataIntegrityAnalyst (obsessed with snapshot freshness, transaction lag, timestamp skew), OptimizationAnalyst (probes objective functions, drift thresholds, transaction cost penalties), ExecutionAnalyst (scrutinizes partial fills, slippage, venue liquidity), RiskConstraintAnalyst (checks pre\u002Fpost-trade limits, compliance blocks), ReconciliationAnalyst (verifies holdings-cash sync, tax lot updates), and PortfolioCoordinator (synthesizes agreements\u002Fcontradictions into root causes, causal chain, fixes). This debate reveals how execution friction invalidates optimization outputs or stale data amplifies reconciliation gaps, building credible explanations through challenge.",[17,5795,5797],{"id":5796},"guided-phases-drive-efficient-convergence-over-rigid-rounds","Guided Phases Drive Efficient Convergence Over Rigid Rounds",[22,5799,5800],{},"Reject sequential (too rigid, ignores back-influence like reconciliation questioning data), parallel fan-out (misses reactions), or supervisor-worker (pre-frames issues) patterns. Use guided discussion: agents as strategies, orchestrator as mediator. Structure in phases—(1) broad independent analyses, (2) coordinator spots contradictions\u002Funresolved questions (e.g., 'Does ExecutionAnalyst's partial fills explain OptimizationAnalyst's non-convergence?'), (3) targeted clarifications from relevant agents only, (4) final synthesis—stops when coherent, not at fixed MAX_ROUNDS=12 (6 agents × 2 passes). This mirrors natural reasoning: not every agent needs equal turns; focus tension between two (e.g., risk blocks vs. execution slippage) adds insight without filler.",[17,5802,5804],{"id":5803},"semantic-kernel-code-builds-traceable-validated-orchestration","Semantic Kernel Code Builds Traceable, Validated Orchestration",[22,5806,5807],{},"Initialize with OpenAIChatCompletion (gpt-4o), InProcessRuntime. Define agents via ChatCompletionAgent with strict instructions enforcing role isolation (e.g., DataIntegrityAnalyst ignores optimization unless data-dependent; no 'I agree' shallow replies). Order: data → optimization → execution → risk → recon → coordinator for rhythmic flow. Use GroupChatOrchestration with RoundRobinGroupChatManager for baseline, or async phase-based invoke() chaining detect_unresolved_questions() → build_followup_prompt(). Callback logs structured responses (agent, content) for inspection. Validate outputs: flag \u003C120-char replies or identity leakage (e.g., OptimizationAnalyst mimicking ExecutionAnalyst). Scenario prompt activates all: drift during volatility, stale snapshots, cost hikes, partial fills, recent changes. Yields report with root causes (confidence\u002Frationale), causal chain (e.g., stale inputs → optimizer over-allocates equities → risk resizes → incomplete recon), fixes (immediate: refresh cadence; medium: input validation; long: dynamic thresholds).",{"title":31,"searchDepth":45,"depth":45,"links":5809},[5810,5811,5812],{"id":5789,"depth":45,"text":5790},{"id":5796,"depth":45,"text":5797},{"id":5803,"depth":45,"text":5804},[385],{},"\u002Fsummaries\u002Fmulti-agent-debate-unpacks-portfolio-drift-causes-summary","2026-04-08 21:21:18",{"title":5779,"description":31},{"loc":5815},"bf2c24e1e55eb65f","Generative AI","https:\u002F\u002Funknown","summaries\u002Fmulti-agent-debate-unpacks-portfolio-drift-causes-summary",[414,413,30,415],"Orchestrate domain-specific agents via Semantic Kernel to debate portfolio drift—data integrity, optimization, execution, risk, reconciliation—yielding synthesized root causes from emergent tensions, unlike linear single-agent analysis.",[415],"rvnjFJv9OQs0XBK5nJC4XLLQq3Bzc_jEQYUyA9BvIWA"]