An AI assistant can help you write code, create tests, and review documentation. But asking one assistant to do everything at once is not always the best approach. When work requires different criteria and independent checks, assigning clear responsibilities to specialized agents may help.
The goal is not to create as many agents as possible. It is to define focused responsibilities, provide the right context, and establish how results will be checked.
Here are five things worth understanding before introducing specialized agents into a software project.
1. A specialized agent has a defined responsibility
A specialized agent is an AI system configured to handle a particular class of tasks. It may combine a language model with instructions, context, tools, and execution rules.
A reviewer agent, for example, might be tasked with evaluating code changes, identifying security risks, and finding maintainability issues. Its job is not to build the entire feature but to assess work produced elsewhere.
Specialization does not necessarily require training a new model. It often comes from the agent’s configuration and available resources. That configuration does not automatically make the agent an expert or guarantee that its conclusions are correct.
2. Specialization separates different evaluation criteria
A project might define these roles:
- Developer: implements changes from a task and its acceptance criteria.
- Reviewer: evaluates readability, design, quality, and potential security issues.
- QA: prepares or runs checks to uncover defects.
Each role operates with different priorities. A developer focuses on delivering the requested behavior; a reviewer challenges the implementation; a QA agent looks for evidence that the behavior works.
The separation can make work easier to reason about, but it introduces coordination overhead, runtime, and cost. For a small change, one agent plus a human reviewer might be enough.
3. More context does not always lead to better results
A common mistake is sending the entire repository and every previous conversation to every agent. Irrelevant material can increase cost and make critical details harder to identify.
Provide the minimum sufficient context for each task: the goal, relevant files, project conventions, constraints, and a clear definition of completion.
A reviewer working on a .NET API change needs to understand the affected contracts, security expectations, and related tests. Unrelated mobile application documentation may add little value.
Context must be proportional to the task. Too little can hide important dependencies; too much can reduce focus.
4. Tools extend what an agent can actually do
A model can explain how to run a test. An agent with authorized tools can run the command and inspect the result.
Depending on its setup, an agent may:
- Read files and search repository documentation.
- Inspect references and version-control diffs.
- Run tests, static analysis, or builds.
- Propose code changes and prepare a review summary.
There is a critical difference between suggesting an action and actually performing it. When an agent claims a test passed, there should be evidence that it ran and a record of the result.
Tool access creates risk as well. Apply least-privilege permissions, protect secrets, and require approval for sensitive actions such as modifying live data, deploying software, or accessing external services.
5. Multiple agents can collaborate, but coordination matters
Imagine a request to add a new API feature:
- The Developer agent implements the change.
- The Reviewer evaluates the code and sends findings back when issues appear.
- The QA agent checks behavior using available tests.
- Problems found during review or testing are returned to Developer for fixes.
- Relevant checks are repeated before the result is submitted to a person.
This is a conceptual workflow, not a required architecture. Some systems perform tasks concurrently or add approval checkpoints. The workflow defines transitions and sequencing; the agents perform the assigned work.
The end state should be “Ready for human review.” Passing automated checks does not by itself prove a change is safe, complete, or ready for production.
How to get started without overengineering
You do not need a full multi-agent platform to test the idea:
- Pick a repeatable task, such as reviewing a pull request.
- Define one clear responsibility for the agent.
- Specify its inputs and expected output format.
- Grant only the tool access it actually needs.
- Set acceptance criteria and a human review checkpoint.
- Compare results with your existing process before introducing more agents.
A useful reviewer output identifies the file, location, severity, reason, and suggested change for each finding. That is more actionable than generic statements such as “the code could be improved.”
Common mistakes
Treating specialization as accuracy. Instructions to review security are not a substitute for professional security assessment and do not eliminate false positives.
Creating an agent for every tiny step. Too many roles may add more handoffs than value.
Skipping the definition of done. Without acceptance criteria, agents may continue making unnecessary changes.
Giving tools unrestricted access. Commands and external actions need appropriate permissions, limits, and traceability.
Trusting summaries without checking evidence. Review executed tests, actual diffs, and recorded failures when those details matter.
Practical example: adding a .NET endpoint
Suppose you need an endpoint to retrieve information about a resource.
Developer receives the expected contract, project conventions, and relevant files. It implements the endpoint and records what changed.
Reviewer compares the implementation with the requirements, checking input validation, error handling, authorization, database access, and consistency with the existing architecture. It documents issues for correction.
QA runs the available tests and reports which scenarios were verified and which were not. A failure sends the task back for correction and renewed validation.
The output is not “AI finished; deploy it.” It is a proposed change with findings and supporting evidence, ready for a human to review.
Final thoughts
Specialized agents are useful when responsibilities are explicit and their work can be verified. Their value comes less from the number of agents and more from the quality of tasks, context, tools, and controls around them.
Next in this series: what an AI harness is and how it relates to executing and supervising these workflows.