#reliability
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ClaimReceipt: Verifying Agent Evidence Sufficiency and Coverage
ClaimReceipt is a framework designed to evaluate AI agents by verifying that their outputs are supported by sufficient evidence and cover all necessary requirements, addressing the reliability gap in agentic workflows.
AI Agents Are Distributed Systems: Managing Failure and State
When AI agents interact with external systems, they cease to be just models and become probabilistic coordinators. To prevent production failures, you must apply distributed systems principles like idempotency, scoped credentials, and circuit breakers.
AI EngineerBuilding Deterministic Infrastructure for Autonomous AI Agents
Reliability in agentic systems is an infrastructure challenge, not a model one. To scale agents, you must build a 'control plane' that separates model reasoning from production execution via validation, policy enforcement, and circuit breakers.
AI EngineerAutomating ETL Pipeline Recovery with RL Agents
A reliable, safety-first architecture for ETL pipeline remediation that uses deterministic anomaly detection, Q-learning for action selection, and an external safety layer to reduce MTTR by 99.85%.
RL-Guided ETL Pipeline Remediation: Architecture and Evals
Automate ETL failure recovery using a deterministic anomaly detection layer, a Q-learning policy for action selection, and a hard-coded safety guardrail to ensure operational reliability.
Turning Python Scripts into Reliable Production Systems
Moving from a one-off script to a production system requires shifting focus from simple execution to reliability, observability, and operational discipline.
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