Agent Harness Engineering & Reliable Automation
Move from "the agent can finish a task" to "the agent keeps running reliably": learn state management, agent loops, state machines, checkpointing, context compaction, verification, human-in-the-loop, retry, failure recovery and observability, design agentic automation that can be interrupted, resumed and verified, and complete your own Agent Harness & Reliability Canvas.
Course storyline: how an agent keeps running reliably
The whole course follows one line: State, Control, Checkpoint, Verify, Recover, Observe, Govern.
- 1. State
What must the system remember about execution?
- 2. Control
What decides which step runs next?
- 3. Checkpoint
Where can execution safely pause and resume?
- 4. Verify
How does the system know a result is acceptable?
- 5. Recover
What happens when execution fails?
- 6. Observe
How can humans understand what happened?
- 7. Govern
What limits keep the system safe and controllable?
Your next step
AI 302 · Module 1: What Is an Agent Harness?
Curriculum
- Module 1What Is an Agent Harness?Why does a capable agent still need an execution control layer?
- Module 2State, Loops & State MachinesWhat must the system remember about execution, and what decides the next step?
- Module 3Checkpointing, Resume & Durable ExecutionWhere can execution safely pause, and how does it resume without repeating actions?
- Module 4Verification, Human-in-the-Loop & Control GatesHow does the system know a result is acceptable, and which steps need a human?
- Module 5Retry, Failure Classification & RecoveryWhen execution fails, should it retry, fall back, escalate or stop safely?
- Module 6Context Compaction & Long-Running AgentsAfter a long run, what stays, what is summarized, what is archived and what can be retrieved?
- Module 7Observability, Reliability & Operational BoundariesHow can humans understand what happened, and which limits keep the system controllable?
- Module 8Agent Harness & Reliability CanvasHow do you bring state, checkpoints, verification, recovery and observability together into one design you can take back?
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Practice and assessment
- Activity 1: Agent Logic or Harness?Judge whether a design belongs to agent reasoning, workflow, harness control, a tool or human control.Not done yet
- Activity 2: Build the State ModelTurn an agentic workflow into states, transitions, error states and human review states.Not done yet
- Activity 3: Where Should We Checkpoint?Spot where to checkpoint, where to resume and where duplicate execution could happen.Not done yet
- Activity 4: Automatic or Human Gate?Design verification gates and human-in-the-loop control points.Not done yet
- Activity 5: Retry, Recover, Escalate or Stop?Design retry, fallback, escalation and safe stop for different failure types.Not done yet
- Activity 6: Compact the ContextRework a long agent execution history into a working context that can keep going.Not done yet
- Activity 7: What Should We Observe?Define the minimum observability needed for runs, states, tool calls, errors, recovery and human interventions.Not done yet
- AI 302 assessmentTwo levels: knowledge checks and reliability design scenarios.Not done yet
- Agent Harness & Reliability CanvasBring state, checkpoints, verification, human-in-the-loop, recovery, compaction and observability together in an Agent Harness & Reliability Canvas you can take back to industry and system development work.Not done yet
About this course
Outcomes
- Explain the difference between an agent harness and agent logic
- Represent an agentic workflow as states, transitions and controlled loops
- Design checkpoint, resume and durable execution strategies
- Add verification and human-in-the-loop controls to high-risk steps
- Distinguish failure types and design retry, fallback, recovery and safe stop
- Design context compaction for long-running agents
- Define observability and reliability metrics
- Complete an Agent Harness & Reliability Canvas
Prerequisites
- AI 202 is recommended, or a basic grasp of agentic workflows, planning, reasoning, state, action and adaptive planning
- Having finished AI 301 makes it easier to bring multi-agent systems into harness and reliability design
- Understanding the tool, permission and external action concepts of AI 201 is recommended
- No familiarity with the LangGraph API is required
- No distributed-systems or DevOps background required
- The course focuses on reliable agent architecture, not on operating any specific framework
Who it is for
- Learners who finished AI 202 and want to move an agent from demo to a reliable system
- Students and researchers designing agentic workflows, AI copilots, intelligent automation or long-running processes
- Coordinators and managers responsible for AI system adoption, process automation, AI governance or production readiness
- System planners who need to design human-in-the-loop controls, checkpoints, failure recovery or observability
- Teams taking an AAIIA project or an industry-academia proof of concept toward a sustainably running MVP
We suggest finishing AI 202; having finished AI 301 makes it easier to bring multi-agent systems into the design. Understanding the tool, permission and external action ideas of AI 201 also helps. These are suggestions only and never block you from starting.
Certificate
Earns a course completion certificate (L1); a capability certificate (L2) is also available through additional artifact review.
Requirements to earn it
- Complete all 8 course modules
- Pass the post-test assessment at 80% or higher
- Submit a project artifact and pass review
Related courses
AI 302 follows AI 202 and AI 301 and deals with how execution is controlled, persisted, verified, recovered and observed. Only courses that exist are linked.
- AI 104: Agentic AI Workflow & Execution Design
It asks how an Agentic Workflow should execute. AI 302 deepens state, checkpoints, durable execution, recovery and observability.
- AI 202: Agentic AI Planning & Reasoning (recommended prerequisite)
Decision intelligence: planning, reasoning, choosing the next action and re-planning. AI 302 is about execution reliability.
- AI 301: Multi-Agent Collaboration & Orchestration (recommended preparation)
Roles, delegation, communication and coordination. AI 302 can support a multi-agent system but does not reteach role design.
- IL 101: Industry AI Co-creation Lab (bring what you learned into a real industry project)
AI 302 is about execution reliability for a single agent system; IL 101-302 is the applied series that takes a real industry problem from project charter, PoV and readiness through pilot and roadmap.
Academic mapping: M5276 / AAIIA Week 14
AI 302 maps mainly to Week 14 of M5276 Agentic AI Innovative Applications: Agent Harness and Loop Engineering for Durable Workflow Automation (LangGraph, State Machines, Checkpointing, Context Compaction, Verification, Human-in-the-Loop and Failure Recovery).
| AAIIA Week 14 topic | AI 302 module |
|---|---|
| Agent Harness | Module 1 |
| Loop Engineering | Module 2 |
| LangGraph (implementation example) | Modules 2 to 4 |
| State Machines | Module 2 |
| Checkpointing | Module 3 |
| Durable Workflow Automation | Modules 3, 5 and 7 |
| Context Compaction | Module 6 |
| Verification | Module 4 |
| Human-in-the-Loop | Module 4 |
| Failure Recovery | Module 5 |
| Reliable Operation | Modules 5 to 7 |
Suggested sequence
- AAIIA Week 10 / AI 202: Planning & Reasoning
- AAIIA Week 13 / AI 301: Multi-Agent Collaboration
- AAIIA Week 14 / AI 302: Harness & Reliable Automation
- AAIIA Weeks 15 to 16: Final Project / Industry MVP
LangGraph is only one implementation example. The course teaches a framework-independent Reliable Agent Architecture.
How AI 302 differs from neighbouring courses
All of them are agent courses, and each answers a different question.
| Course | Core question | What AI 302 adds |
|---|---|---|
| AI 104 | How should an Agentic Workflow execute? | Deepens state, state machines, checkpoints, durable execution, recovery and observability, and operational reliability. |
| AI 202 | How do we plan, reason and choose the next action? (decision intelligence) | Focuses on execution control, persistence, safe resume, verification, recovery and observability. (execution reliability) |
| AI 301 | How do several agents divide work, delegate, communicate and coordinate? | Focuses on state, checkpoints, durable execution, verification, failure handling and operational control. It can support a multi-agent system but does not reteach roles or orchestration. |
AI 202 = decision intelligence. AI 302 = execution reliability.
Course feedback
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