Six modules down. You now know what agents are, how they use tools, how they manage memory, how multiple agents coordinate, why they fail, and how to build one. This capstone turns that knowledge into something real: three practical challenges that result in a functioning agent design for your own work.
What You Covered
Module 1
What Is an AI Agent
The four properties: perception, reasoning, action, feedback. The ReAct loop. Why agents are more powerful and more risky than chatbots.
Module 2
Tool Use and Function Calling
JSON schemas, read versus write tools, code execution tools, and the Toolformer approach to teaching models tool judgment from examples.
Module 3
Memory and Context
In-context, external retrieval, episodic, and procedural memory. Why context windows limit agents. Summarization and external note-taking as the main workarounds.
Module 4
Multi-Agent Systems
Orchestrator-worker, peer-to-peer, sequential pipeline, and critic-reviewer patterns. Benefits and risks of splitting work across multiple agents.
Module 5
Agentic Failure Modes
Goal drift, infinite loops, tool misuse, compounding errors, prompt injection, and scope expansion. Minimal footprint and human-in-the-loop checkpoints as defenses.
Module 6
Building Your First Agent
The six-step build process: define task, list tools, write system prompt, set limits, dry-run test, iterate. The pre-launch checklist and handoff conditions.
Your Three Practical Challenges
These challenges take you from understanding to doing. Each one is designed to produce a real output you can use or deploy this week.
Three challenges to complete today
1
Design your agent on paper. Pick a specific task from your work. Write: the goal in two sentences, the tools needed (name and read/write), the system prompt, and the exit conditions. Do not skip the system prompt; writing it will reveal gaps in your thinking that code never will. This design document is the agent.
2
Map the failure modes. For your agent design, go through the six failure modes from Module 5. For each one, write one sentence describing how it might occur in your specific agent, and one sentence describing your defense. If you cannot write a defense, redesign that part of the agent before building it.
3
Define one human checkpoint. Find the one action in your agent's loop that would be hardest to reverse if wrong. Write the exact condition under which the agent should pause and ask for human approval before proceeding. Make it specific: not "when uncertain" but "when the proposed email recipient is not in the approved contacts list."
After completing the three challenges, you have a deployment-ready agent design. Building it in code is the next step, but it will be far faster and less error-prone because the hard thinking is already done.
Your Certificate
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AI Agents and Agentic Systems
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