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1. Turn the epic into an outcome a reviewer can verify
Do not hand an agent an epic and expect it to infer the product decision, repository conventions, and acceptable trade-offs. First describe the change in terms a developer and reviewer can both check.
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- Outcome: What user or system behavior should change?
- Acceptance criteria: What observable results show the task is complete? Include relevant success cases and failure cases.
- Constraints: Which interfaces, compatibility requirements, performance expectations, or design decisions must remain intact?
- Repository context: Point to relevant components, existing patterns, documentation, and commands for the checks the project uses.
- Known risks: Identify areas that need special care, such as data changes or security-sensitive behavior.
Keep the first delegated unit small enough that a person can understand and review its diff. GitHub’s getting-started guidance for Copilot agents likewise begins with a small issue rather than an open-ended project.
2. Decompose the epic before authorizing implementation
For work crossing multiple components, ask for repository analysis and a proposed implementation plan before asking an agent to make a sequence of changes. Review the plan as an engineering decision: confirm that it addresses the acceptance criteria, fits the existing architecture, and has a sensible order.
Then split the work into reviewable issues and record prerequisites. Some tasks may be analysis-only—for example, identifying callers or mapping a migration—and need not produce code. OpenAI’s description of Symphony illustrates an orchestrated approach in which tasks can form a dependency tree and blocked work waits for its prerequisites. That is a useful pattern, not a requirement that every team adopt that system.
An issue tracker can serve as a workflow control plane: it records what work exists, who or what is handling it, and which tasks are blocked. If two tasks touch the same interface or depend on the same design choice, establish the prerequisite first instead of letting agents race to implement incompatible assumptions.
3. Assign a bounded task with explicit scope
Whether a developer starts each session or an agent picks up assigned issues, the task should specify what the agent may change and what it must leave alone. Include the acceptance criteria, relevant repository instructions, expected checks, and known dependencies in the issue or task context so the agent does not have to guess.
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- Out of scope: Neighboring refactors, unrelated cleanup, or decisions that need a maintainer.
- Completion evidence: The tests, checks, or explanation the agent should provide.
- Escalation point: What to do if the implementation requires a change to the agreed design or acceptance criteria.
These are workflow controls a team can apply across tools; they are not universal vendor requirements. GitHub documents assigning an issue to Copilot and having it work toward a pull request, while Symphony describes issue-driven orchestration. Both illustrate why the task record should remain understandable to people even when an agent does much of the implementation.
4. Set access and safety boundaries before work begins
Permissions are part of the task definition, not an afterthought. Configure repository access, tools, credentials, and network availability according to what the task actually needs. Require explicit approval for higher-risk actions, and retain logs that let the team inspect requests, approvals, tool execution, and policy decisions.
OpenAI describes these controls in the context of its own Codex deployment in “Running Codex safely at OpenAI”. Agent capabilities and defaults differ by product, host, and configuration, so do not assume that one vendor’s boundary or network setting applies to another. OpenAI’s launch description of Codex included a network-disabled cloud container; that was a launch configuration, not a timeless guarantee about every current setup.
5. Monitor the run and intervene when needed
Delegation is not the same as abandoning the task. Follow the session output and inspect which files the agent reads and changes. If it misunderstands scope, reaches a blocker, or starts making a decision that belongs to the team, steer it back, revise the task, or stop the run.
GitHub’s documented Copilot agent workflow includes live updates, session logs, and steering prompts. OpenAI’s account of Symphony identifies context switching and stalled sessions as operational bottlenecks in its own experience. Those observations make monitoring useful even when a task is nominally autonomous: a short intervention can prevent an incorrect assumption from turning into a large diff.
6. Validate the implementation against evidence, not a status summary
When the agent reports completion, compare the actual change with the acceptance criteria and inspect the evidence behind its summary. A passing test suite is useful evidence, but it cannot establish that the tests cover the important behavior or that the implementation matches the intended design.
- Inspect the diff. Check changed files, public interfaces, error handling, and any behavior outside the task’s scope.
- Run the relevant checks. Use the project’s required tests, linters, builds, or other automated checks; inspect failures instead of treating a green summary as sufficient.
- Trace criteria to results. For each acceptance criterion, identify the code or test that supports it. Note any criterion that remains unverified.
- Record unresolved questions. Send design ambiguity, missing coverage, and follow-up work to a human rather than allowing the agent to silently redefine “done.”
OpenAI’s Codex launch article describes inspectable citations, terminal logs, and test results, and says people should manually review and validate generated code before integration and execution. Treat such output as review evidence, not as a substitute for reading the change.
7. Use the pull request as the review boundary
The pull request should make the proposed change reviewable independently of how it was produced. Read the real diff, assess it against the task, and request changes or edit it when necessary. Keep iterations on the same branch or pull request where the workflow supports it, so reviewers can see how the proposal changes in response to feedback.
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In GitHub’s documented Copilot flow, the agent opens a pull request and adds a person as reviewer; that person can request changes, make edits, approve, and merge when satisfied. A second AI review may help find issues, but it does not replace an accountable human review or the team’s own approval rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Make merge authorization a separate decision
Producing a change is not permission to merge it. The person or role with merge authority should decide whether the review, required checks, and project policies have been satisfied. Record whether a human approved the change and keep any out-of-scope improvements as separate follow-up work, rather than expanding a task’s scope at the last moment.
A 2026 preprint by Young Jo, Chung, and Safwat Hassan analyzed 29,585 pull-request lifecycles across five coding-agent tool families. In that dataset, terminal merge authority remained predominantly human; the paper also reports at least 96% agent-initiated PRs for its “Collaborator” tool group and at least 95.6% human-initiated PRs for its “Assistant” group. These are results for the study’s sampled tools and definitions, not a rule for all products or teams. The distinction is useful: an agent may initiate or implement work without holding approval or merge authority. See the preprint.
Choose tools by workflow fit, not headline claims
There is no controlled, current benchmark in the cited material that establishes one coding agent as best for every team. Compare candidates against the workflow you need to operate:
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- Task structure: Can work be split into dependent tasks, and can analysis-only work be represented?
- Execution boundary: What repository, tool, credential, and network access can be granted or restricted?
- Observability: Can a developer inspect session activity, diffs, test output, and audit events—and steer or stop the run?
- Review and merge governance: Who can request changes, approve, and merge?
- Operational cost: Account for model usage, CI or Actions minutes, and the human time needed to review and recover. GitHub documents AI-credit and Actions-minute use for third-party agent sessions; costs depend on factors including model and tokens processed, and plan eligibility or billing details can change. Check the current GitHub documentation for applicable terms.
OpenAI reports a 500% increase in landed pull requests on some teams using Symphony. That is an organization-reported outcome for some teams, not an independently controlled evaluation or a productivity gain other teams should expect.
Run a bounded pilot and learn from the review load
Start with a task whose outcome and checks are clear, and use the complete workflow: issue, scoped assignment, monitored run, validation, pull-request review, and deliberate merge decision. Track both engineering outcomes and the work required to review, correct, or recover from the agent’s changes. Use review feedback and failed checks to improve task instructions and repository safeguards before expanding to more consequential work.
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