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Agent Engineer

Build agents that reason, call tools, and stay reliable across many steps.

For Engineers building tool-using agents · 16 articles · ~24h · 4 stages

This track assumes you already know

  • What Is a Token in AI?
  • What Is an LLM?
  • Prompt Basics
  • Getting JSON Out of an LLM
  • Why Do AI Models Hallucinate?
  • What Is a Context Window?
  • Making a Model Think Step by Step
  • How RAG Works, Step by Step
  • How Do You Know It Works?
  1. 1

    Agent foundations

    • What Is an AI Agent?Strip the hype and an agent is three things: a model that can call tools, a loop, and the authority to decide what happens next.Agents
    • How Does Tool Calling Work?The model never runs anything. It emits a structured request, your code executes it, and the result goes back as text. Everything follows from that.Agents
    • Writing Good Tool DefinitionsThe tool description is a prompt, and it is the one the model relies on most. Vague descriptions cause more agent failures than weak reasoning.Agents
    • What Is MCP?A standard protocol for exposing tools to models, so integrations stop being written once per application.Agents
  2. 2

    The loop

    • The Agent LoopThink, act, observe, repeat. The cycle is trivial to implement and the hard parts are all about when to stop.Agents
    • The ReAct PatternInterleave reasoning and action instead of separating them. The idea behind the modern agent loop, why it beats reasoning alone, and where it strains.Agents
    • Multi-Step PlanningPlan upfront or decide as you go? The tradeoff is coherence against adaptability, and the practical answer is usually a shallow plan you revise.Agents
    • Agent Architectures: From Rules to AutonomyA spectrum runs from fixed pipelines to fully autonomous loops. Knowing where a task belongs on it is the single most important agent design decision.Agents
  3. 3

    Make it reliable

    • How Agents FailSeven recurring failure modes, what causes each, and the specific guardrail that prevents it. Read this before shipping anything autonomous.Agents
    • Human-in-the-Loop DesignWhere to put a person in an automated system. Gate by reversibility and confidence, not by task importance.Advanced Agents
    • Agent MemoryModels have no memory between calls. Everything that feels like memory is retrieval, and designing what to store is the actual problem.Advanced Agents
    • Context EngineeringDeciding what goes into the context window, in what order. The discipline that replaced prompt engineering once systems got complicated.Advanced Agents
  4. 4

    Go further

    • Multi-Agent SystemsSeveral agents with different roles, coordinating. Sometimes better than one, often just a more expensive way to fail.Advanced Agents
    • Coding AgentsThe agent application that works best in practice, and the specific properties of software work that make it work.Advanced Agents
    • Evaluating AgentsScore the outcome, not the path. Agents are nondeterministic and multi-step, which breaks the evaluation habits that work for single calls.Advanced Agents
    • Prompt InjectionInstructions and data arrive as the same thing. That is the vulnerability, it has no complete fix, and the defense is architectural.Security
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