# How to design AI agent teams around real business work https://palnau-systems.vercel.app/insights/designing-ai-agent-teams Published 2026-11-10 by Oliver Palnau and the Palnau Systems team. Prepared with AI assistance. Audience: Operations leaders and teams building AI workflows. An AI agent team works best when each role has a distinct responsibility, a limited toolset and a defined output that another role can verify. Start with the workflow and split it only where specialization improves quality or control. Give one coordinator responsibility for the final result and keep business authority outside casual conversation between agents. More participants can create more cost and coordination failure. ## Key takeaways - Separate roles by responsibility or access boundary, not by impressive job titles. - Require structured handoffs with evidence and unresolved questions. - Evaluate the full workflow as well as each specialist. ## Choose meaningful boundaries A useful split might separate document extraction from eligibility checks and customer communication. The extractor handles source material; the checker applies approved rules; the communicator drafts an explanation. Each role has a different quality test. In contrast, assigning three agents to brainstorm the same email may add little beyond extra review. Write down what becomes easier after the split. Is it a smaller permission set, a clearer test, an independent calculation or parallel work on separate evidence? If the answer is only that it resembles a department, keep the design simpler until a measured problem justifies additional roles. ## Specify the handoff before writing the prompts Every handoff should contain a case identifier, source references, the completed output, unresolved issues and a status. Define what happens when a required field is missing. Free-form messages such as “looks good” hide uncertainty and force the next agent to reconstruct the work. Avoid passing an entire conversation when a concise evidence packet is enough. For a hypothetical commercial proposal workflow, the research role supplies verified company facts and source dates. The scoping role maps customer needs to approved services. A pricing calculator supplies numbers under fixed rules. The proposal role assembles a draft, and a person approves the commercial terms. Nobody can quietly change another role’s evidence. ## Make coordination an explicit responsibility The coordinator needs a completion checklist, a shared deadline and a total budget. It should know when to ask for one revision, when to stop and when to escalate. Without these limits, specialists can send work back and forth while the customer waits. A cycle limit is a practical operational control, not just a way to reduce model spend. Keep durable case state in a system that records which steps succeeded. If a worker times out, the coordinator should resume from the last verified state rather than re-running every action. Distinguish a draft that may be regenerated from an external action that could produce a duplicate order or message. ## Test the seams and the overall result Build cases where one specialist provides incomplete evidence, another disagrees, or an outside system is unavailable. Check whether the coordinator catches the problem. A team can produce excellent individual outputs and still fail because the wrong customer record was passed between roles. Copy-ready handoff brief: “Return the case ID, completed fields, exact source references, validation results and remaining uncertainties. Do not silently repair contradictory source facts. Mark the handoff ready only if the required fields pass; otherwise name the missing input and the responsible next owner.” Use that contract in both tests and daily operations. ## First steps 1. Draw the workflow and justify each proposed specialist. 2. Give each role a precise output and a minimum toolset. 3. Define a shared case identifier and handoff schema. 4. Set revision limits, cost limits and escalation ownership. 5. Test missing evidence, worker failure and duplicate-action scenarios. ## What to measure Handoff acceptance: Measure the share of specialist outputs accepted by the next stage without clarification or repair. End-to-end quality: Score the final business deliverable against a human-approved rubric, including source consistency. Coordination overhead: Track time and usage spent on retries, routing and revisions compared with useful task execution. ## Should agents debate every decision? Only when disagreement serves a defined quality check and you can evaluate whether it helps. Repeated debate can consume time without improving accuracy. For calculations and policy rules, deterministic checks and a qualified reviewer may be more useful than additional opinions. ## Can different roles use different AI models? They can, but each combination needs testing. Keep the handoff format stable and compare the complete workflow, including latency and failure behavior. A cheaper specialist is not a saving if its output creates extra work downstream. ## Sources and further reading - [Anthropic: Building effective agents](https://www.anthropic.com/research/building-effective-agents) - [Anthropic: Writing effective tools for agents](https://www.anthropic.com/research/writing-tools-for-agents) ## Work directly with Oliver and his team Work directly with Oliver and his team to define useful agent roles, connect the required systems and test whether the team improves a real business outcome. Free introductory call: https://calendly.com/oliverpalnau/30min Services and scope: https://palnau-systems.vercel.app/services Current pricing: https://palnau-systems.vercel.app/pricing