Agent Building

These concepts cover agent systems reliable enough for real product or business use: architecture, coordination across tools and agents, memory, security, and the production lifecycle of evaluation, monitoring, and deployment. The learner should be able to implement agents that manage complex workflows, coordinate several tools or agents with clear ownership, resist prompt injection, enforce permissions in code, and improve through measured evaluation and monitored deployment.

Key Concepts and Activities

1. Agent Architecture and the Planner-Executor Pattern
   • Activities: Split the system into planner, executor, tool layer, memory, and validation with defined interfaces, where the executor can only run planned tasks.
   • Reason: Boundaries make the system debuggable and stop the executor inventing unapproved work.
   • Example task: Build a research agent as separate modules where the executor is swappable and any new work idea goes back to the planner as a replan request.
2. Orchestration, Retrieval, and Multi-Agent Handoffs
   • Activities: Chain multiple tools passing outputs between them, resolve conflicts between results, ground answers in retrieved content with sources, and where roles are split across agents, define a structured handoff with one agent owning the final answer.
   • Reason: Workflows cross systems and actors; retrieval grounds answers in trusted data, and clear handoffs plus ownership stop multi-agent setups from failing at the seams.
   • Example task: Build a policy pair where a research agent reads a document and checks a database (with a rule for when they disagree), then hands structured, source-cited findings to a writing agent that owns the final output.
3. Memory
   • Activities: Implement session and long-term memory as separate stores with rules for what is saved, read back, and never stored.
   • Reason: Memory is a data system with privacy risk, so never-store rules must live in code.
   • Example task: Build a memory that retains user preferences across sessions while filtering out passwords, financial data, and personal identifiers.
4. Guardrails, Prompt Injection Defense, and Human-in-the-Loop
   • Activities: Enforce read/write/delete permissions in the tool layer, treat external content as untrusted, and route risky or failed cases to a human queue with full context, resuming after approval.
   • Reason: The model can be tricked, so enforcement and escalation must sit in code that can't be bypassed.
   • Example task: Build an email agent where a test email with hidden instructions triggers no tool action, and refunds above a limit go to a human queue, then resume after approval.
5. Observability, Evaluation, and Deployment Lifecycle
   • Activities: Log every plan, tool call, decision, and retry; track cost and success rate; run automated eval sets on every change; route easy steps to smaller models or plain code; and version everything together with monitoring and rollback after launch.
   • Reason: Logs are the only way to debug production, evals make every change measurable, and behavior drifts, so maintenance never ends.
   • Example task: Profile a workflow to find a failing step from logs alone, move one wasteful step from a model call to plain code, run a 20-case eval before and after the change, and create a launch checklist with alerts and a rollback step.