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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
      • Memory Management in Agents

Agent Engineering

A futuristic robot carefully filters digital information, rejecting noisy chat fragments on the left while storing selected, verified memory blocks in a secure glowing vault on the right, illustrating selective long-term memory in AI agents.

Long-Term Memory in Agents: What Should Actually Persist

AI Agent Engineering for Developers, Multi-Agent SystemsBy Sami29.07.2026Leave a comment

Short-term state is not a transcript. It is the working memory of the current task. This article explains how clean, structured thread state improves agent focus, reduces context pollution, and makes tool-using systems easier to debug and scale.

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Futuristic split-scene illustrating the evolution from prompt-based AI interactions to agent engineering. On the left, a small robot works with a laptop and simple conversational interfaces, representing traditional prompting. On the right, a larger advanced AI agent operates within a network of interconnected systems, tools, workflows, memory, evaluation, and orchestration components. A glowing stream of energy bridges the two sides, symbolizing the transition from isolated prompts to structured, production-ready agent systems.

From Prompting to Agent Engineering

AI Agent Engineering for Developers, The Agent LoopBy Sami08.07.2026Leave a comment

Prompting matters, but it is only one control layer. Reliable agents come from designing the loop, validating tool use, tracing decisions, and treating runtime behavior as the real unit of quality.

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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 tools to choose from

Tool Calling Agents: How Models Turn Decisions Into Actions

AI Agent Engineering for Developers, The Agent LoopBy Sami13.05.2026Leave a comment

Tool use is where an agent stops generating text and starts affecting real systems. This article explains why tool design acts as both decision boundary and action contract, and how better schemas, validation, and tracing make tool calling agents more reliable.

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