How should multiple AI engineering agents be orchestrated?

An agent can already plan, edit code, run tests and react to feedback. Adding specialist agents for exploration, implementation, testing or review may keep context focused or allow work to proceed in parallel. It also creates more interfaces at which assumptions can diverge.

Multi-agent orchestration is an emerging engineering pattern, not a virtual team that automatically understands shared purpose. Its design should begin with the work and evidence needed, not a preferred number of agents.

Decide whether decomposition creates value

Map the task, dependencies and acceptance criteria. Use several agents where responsibilities can be separated cleanly, such as read-only codebase exploration alongside an independent security review. Keep one agent or a human-led workflow where the work is tightly coupled, highly ambiguous or small enough that coordination would dominate.

Parallel execution is useful only when outputs can be combined safely. Two agents editing the same files may create conflict and hidden assumptions. Sequential specialisation may be clearer when one result forms the verified input to the next.

Establish a comparison with the simpler approach. More completed subtasks do not necessarily mean faster or better delivery.

Define agents as bounded components

For each agent, specify its goal, inputs, authoritative context, allowed tools, permissions, outputs, stopping conditions and escalation. Use least privilege: a research agent may need read access but no write tools; a test agent need not inherit production credentials.

Keep shared instructions small and consistent. Identify which agent or person resolves conflicting interpretations. Do not assume separate context windows create independent evidence if every agent receives the same flawed premise or generated artefact.

Current GitHub documentation illustrates custom agents with separate prompts, tools and isolated sub-agent contexts. These are product-specific mechanisms; the transferable principle is explicit component boundaries.

Engineer hand-offs and acceptance

Treat every hand-off as an interface. Define the artefact format, required evidence, version and conditions for rejection. An implementation agent should know which approved plan it is following. A review agent should inspect the actual change and relevant source context, not only the implementer's summary.

Keep generation and acceptance meaningfully separate. Automated reviews can supplement tests and human inspection but should not authorise consequential changes alone. A named engineer remains accountable for architecture, quality, security and operability, while Product owns the intended outcome.

Provide a way to stop the whole workflow when one component exceeds scope or produces uncertainty that cannot be resolved.

Observe the system and its failure modes

Record delegation, tool use, inputs, outputs, retries, conflicts, approvals and final changes. Protect logs because agents may exchange code, prompts, vulnerabilities or sensitive data. Monitor cost and time limits as well as technical effects.

Test failure scenarios: an agent returns incomplete work, two agents disagree, an untrusted instruction enters shared context, a tool is unavailable or an early error propagates. Confirm that the orchestrator does not silently choose a plausible answer and continue.

Measure end-to-end quality, lead time, human supervision, rework, cost and recovery. Expand only when specialisation or parallelism provides a repeatable benefit beyond added coordination. Sometimes the best orchestration decision is to remove an agent.

Example

A team trials two agents for a bounded dependency update. A read-only agent maps affected services and proposes a plan. After an engineer approves the plan, an implementation agent changes one repository and runs tests. A separate existing security check supplies additional evidence.

The agents cannot merge, deploy or broaden scope. When the implementation conflicts with the approved plan, the workflow stops rather than asking another agent to improvise. The engineer reviews the change and decides whether the two-agent pattern justified its coordination cost.

FAQs

• Are multiple AI agents the same as a virtual engineering squad?

  The term virtual squad is informal. Several specialised agents may resemble roles in a workflow, but they do not hold team accountability, shared organisational understanding or professional judgement.

• Should one agent review another agent's code?

  It can provide additional feedback, especially with distinct context or tools. It is not automatically independent assurance and should sit inside the risk-based acceptance process.

• Is parallel agent work always faster?

  No. Dependencies, edit conflicts, context transfer, verification and integration can outweigh parallel execution. Measure the complete workflow.