Having multiple specialized agents does not automatically create a reliable process. One agent can write code and another can review it, but important questions remain: who receives the output, what happens when tests fail, how many retries are permitted, and who authorizes integration?
AI Workflows help describe and coordinate that process. They combine agents with deterministic steps, conditions, tools, and human decisions.
1. What is an AI Workflow?
A workflow is a sequence or network of steps that transforms an input into an output. In an AI workflow, one or more steps rely on models or agents to interpret, generate, classify, or act.
For example, a feature request triggers implementation, review, testing, and approval. Rules govern transitions; the model does not have to make every decision.
2. Automation does not require full autonomy
Different steps call for different kinds of control:
- Deterministic: compile a project and check the exit code.
- AI-assisted: propose test cases based on a code change.
- Human: approve a major architecture decision or high-impact operation.
You do not need one autonomous agent to own the whole chain. A mixed workflow can be easier to understand and audit.
3. Coordinating specialized agents
A common example includes a Developer Agent, Reviewer Agent, and QA Agent. Each owns a specific responsibility and produces an output required by the next step.
Coordination depends on clear contracts: changed files, review findings, test results, and statuses such as approved, changes_requested, and failed. Without structured outputs, downstream steps may misinterpret what happened.
4. Conditions, retries, and alternate routes
Workflows do not always advance in a straight line. Review findings may send a change back to development. A failing test may trigger another correction followed by a full recheck.
Loops require limits. After two or three unsuccessful attempts, a workflow might stop, retain evidence, and request human assistance. This is safer than retrying indefinitely.
5. Control and observability
A reliable workflow should answer:
- What was the original request?
- Which agent or component performed each step?
- Which tools were invoked?
- Which tests actually ran?
- Why did the process pass, fail, or stop?
Record states, durations, results, and errors. Avoid unnecessarily placing secrets or sensitive data in logs.
Diagram: from feature request to integration
This is a conceptual design. A production system would also add retry counters, terminal failure states, timeouts, and traceability for each transition.
How it works: request to result
- Input: capture the request and acceptance criteria.
- Implementation: the Developer Agent proposes and edits files in a controlled environment.
- Review: the Reviewer evaluates the diff and reports evidence-based findings.
- Decision: blocking findings send the work back; otherwise, it moves ahead.
- Validation: QA runs available checks and attaches actual results.
- Human approval: a responsible person accepts or returns the change.
- Completion: preserve artifacts, decisions, and the final state.
Practical .NET example
Suppose you need to add validation to an ASP.NET Core endpoint. Define minimum outputs for each stage:
| Stage | Required output |
|---|---|
| Developer | Modified file list and change summary |
| Reviewer | Findings with severity, file, and rationale |
| QA | Executed command, exit status, and failing tests |
| Approval | Decision and reviewer notes |
One advancement rule could be: request human review only after blocking findings are resolved and all required checks have completed successfully.
You do not need a particular framework to start. Scripts and controlled agent calls can handle a small workflow; orchestration frameworks become useful as complexity grows.
Benefits and trade-offs
| Benefit | Trade-off |
|---|---|
| Repeatable process | State design and maintenance |
| Clear responsibilities | Coordination overhead |
| Error visibility | Logging and observability costs |
| Explicit approvals | Slower high-impact operations |
Common mistakes
Chaining agents without contracts. Free-form text may not contain enough reliable information for downstream steps.
Ignoring failure paths. Plan for timeouts, incomplete tests, and malformed outputs.
Allowing unlimited retries. Set retry limits, execution budgets, and escalation conditions.
Removing human approval. Automating a task does not imply permission to deploy to production.
Confusing workflows with harnesses. The workflow defines steps and transitions; the harness provides mechanisms through which an agent interacts with its environment. They can work together.
Final thoughts
AI Workflows turn isolated tasks into understandable, measurable processes. Start small, make inputs and outputs explicit, cap execution, and keep human review where the risks justify it.
The next episode covers five common mistakes when coding with AI, with practical ways to prevent them.