Part 7 of 11

Agent Engineering

Workflows versus agents, tool design, MCP and its security model, memory, human-in-the-loop, long-running agents, multi-agent systems, agent archetypes, voice agents, and choosing a framework.

Chapters
13
Hours
3
Difficulty
Intermediate to Advanced
  1. 7.0intermediate

    Workflow vs agent

    If you can write the control flow in Python, it's a workflow. If the model decides at runtime, it's an agent. Most production wins are workflows.

    10 min
  2. 7.1intermediate

    The five workflow patterns

    Chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer: each with code, a use case, and the failure mode that bites in production.

    15 min
  3. 7.2intermediate

    The agent loop

    Think, act, observe, and the part nobody talks about: how the loop ends. Stop conditions, reflection that actually works, and why production agents look nothing like the demo.

    15 min
  4. 7.3intermediate

    Tool design

    The agent-engineering skill: schemas that make invalid calls impossible, descriptions the model can't misread, errors it can recover from, and the right number of tools.

    10 min
  5. 7.4intermediate

    MCP: Model Context Protocol

    How to expose tools, data, and prompts to any AI host through one open protocol; build a server and client end-to-end.

    10 min
  6. 7.5intermediate

    MCP security

    The four attack classes MCP creates: tool poisoning, cross-server shadowing, rug pulls, and injection via tool results, and the architectural defenses that actually stop them.

    10 min
  7. 7.6intermediate

    Agent memory and task state

    The four places an agent can put information, why most teams use the wrong one, and how to design state that survives a pod restart mid-task.

    10 min
  8. 7.7intermediate

    Human-in-the-loop

    Approval gates as tool calls, confidence-based escalation, and handoff design: the three independent things you have to engineer for selective human oversight to actually work.

    10 min
  9. 7.8advanced

    Long-running agents

    Budgets, kill switches, checkpoints, replay, and the sync vs async choice: the harness work that keeps a multi-minute agent from burning a month's API spend.

    15 min
  10. 7.9advanced

    Multi-agent systems

    When multi-agent topology earns its 15x token cost, when it doesn't, and the single-writer pattern that handles most production work.

    15 min
  11. 7.10intermediate

    Agent archetypes

    Research, Coding, Browser, and Computer Use agents differ in one thing that cascades into everything else: what the model sees at each step.

    10 min
  12. 7.11intermediate

    Voice agents

    The 800ms budget that defines voice agents, the cascaded vs native trade-off, and why barge-in is the hardest correctness problem in the stack.

    15 min
  13. 7.12intermediate

    Choosing a framework

    Frameworks buy state, retries, and tracing. Most teams don't need them. The decision rule for plain SDK, light structure, and full graph runtime.

    10 min