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  • Home
  • About
  • Blog
    • The Pragmatic Programmer Series
    • E-Learning Standards
      • AICC
      • SCORM 1.2
      • SCORM 2004
      • xAPI
    • AI Agent Engineering for Developers
      • The Agent Loop

Agent Reliability

Futuristic AI operations scene showing a robot under pressure at the center of a glowing control environment, surrounded by interconnected panels displaying broken workflows, failed tool calls, degraded performance metrics, system outages, data failures, bugs, and warning indicators. Neon purple, blue, and cyan data paths connect the failures, illustrating the complexity of diagnosing and anticipating production failure modes in AI agent systems.

Production Failure Modes in AI Agents and How to Anticipate Them

AI Agent Engineering for Developers, The Agent LoopBy Sami01.07.2026Leave a comment

AI agents usually do not fail with a dramatic crash. They fail quietly through wrong tool calls, invalid arguments, retry storms, looping behavior, and weak recovery. This article explains where the agent loop breaks and how to design traces, guardrails, limits, checkpointing, idempotency, and evals that catch incidents before users do.

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A robot surrounded by icons of stop and different paths

Retries, Backoff, and Recovery Paths for Tool-Using Agents

AI Agent Engineering for Developers, The Agent LoopBy Sami27.05.2026Leave a comment

Reliable agents are not the ones that never fail. They are the ones that fail into the right path. Here is how to classify tool failures into retry, replan, user input, or hard stop, and why retry policy belongs at the tool boundary.

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A Robot surrounded by check and warning signs

Stopping Conditions: How Agents Know When to Finish

AI Agent Engineering for Developers, The Agent LoopBy Sami20.05.2026Leave a comment

Most bad agent experiences come from bad stopping decisions. Learn how to design stop logic in code with explicit exit states, tool signals, step limits, and traceable runtime policies.

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