Engineering AI Agents
Bounded tool-using loops with durable state, guardrails, and evals
Agents are not chat completions in a while loop. This Deep Dive teaches the engineering discipline behind production agents: bounded execution loops with explicit termination conditions, governed tool portfolios, durable memory and checkpoints, recoverable failure handling, and evaluation methods that measure real agent behavior instead of vibes.
See the Invisible
Interactive simulators visualise what's hidden from view.
Hands-On Labs
Step through executions tick by tick. Manipulate state.
Why, Not Just What
Understand the reasoning behind every design decision.
Quizzes & Cheatsheets
Verify your understanding and keep a quick reference handy.
Get Certified
Earn a shareable certificate to prove your deep expertise.
Become the Engineer Who Supervises AI
As AI generates more code, understanding what that code does becomes more valuable, not less. Someone must verify AI output, debug failures, and make architectural decisions.
Build Your Architectural EdgeFrom a demo that impresses to an agent you can put in production
A tool-using agent looks finished after the first successful run: it picks tools, chains steps, and reaches the goal. Then real traffic arrives, and the same agent loops without making progress, retries an irreversible action, burns its token budget on a dead end, and leaves no trace of why. This Deep Dive treats the agent as a system you engineer: you decide which choices the model makes and which stay in deterministic application control, you bound the loop with budgets and termination conditions, you classify actions by how badly they can go wrong, and you build the evaluation harness that tells you whether a change made the agent better or just different.
What You Will Be Able to Do
Design a loop that always terminates
Define goals, observations, and actions for an agent, set step, token, time, and cost budgets, and choose termination conditions and stop reasons so every run ends in a clean terminal state instead of an unproductive loop.
Make tool use safe by construction
Classify each action as read-only, reversible, or irreversible, scope least-privilege credentials per tool, place approval gates on consequential actions, and pair retries with idempotent actions and compensating actions so a failed run does not corrupt real systems.
Prove agent behavior with evals
Assert on task outcomes, constraints, and side effects, analyze trajectories and tool selection, inject tool faults and adversarial instructions, and use nondeterministic trial sets to state a success rate you can defend.
What's Covered
The cycle of goals, observations, actions, and environment feedback, how agent state persists across repeated model invocations, and the line between model decisions and deterministic application control, including when a fixed workflow beats dynamically selected actions.
Minimal tool portfolios with preconditions and postconditions, dependency-aware ordering, task decomposition into verifiable subgoals, and replanning when observations contradict the plan, with search breadth and execution depth traded against budget.
Working context versus durable memory, episodic records, semantic facts, and procedural state, write criteria with provenance, retrieval scoped to the current goal, and the operational edges: staleness, contradiction, tenant isolation, retention, and deletion.
Explicit state machines with persisted checkpoints, retry policies matched to failure class, duplicate suppression, policy enforcement outside the model, and human escalation that resumes with full context rather than starting over.
Outcome and side-effect assertions, efficiency metrics across steps, tokens, latency, and cost, fault injection for unavailable dependencies, and turning production traces into evaluation cases with privacy controls intact.
The Curriculum
Comprehensive Lessons! Each with theory, interactive simulation, and quiz.
The Agent Execution Loop
Governing Agent Tool Use
Planning Under Uncertainty
Memory That Earns Its Keep
Guardrailed and Recoverable Execution
Evaluating Agent Behavior
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