AI Multi-Agent System Designer

How It Works

Describe the end-to-end task, the natural stages it breaks into, and where a human should check the work. The blueprint returns a role for each agent, what it hands off to the next agent, and the guardrails that keep the chain from compounding errors silently.

What to Provide

Multi-Agent Blueprint

Replace [[tokens]] with your details. This is the finished deliverable.

Agent 1: [[Researcher]]

Role: [[Gathers and structures raw information relevant to the task]]

Input: [[Topic or query]]

Output (hands off to Agent 2): [[Structured findings: bullet list with sources]]

Failure mode to guard against: [[Hallucinated facts: require sources for every claim]]

Agent 2: [[Writer/Builder]]

Role: [[Turns structured findings into the actual deliverable]]

Input: Agent 1's output

Output (hands off to Agent 3): [[Draft deliverable]]

Failure mode to guard against: [[Drifting off the original brief: re-inject the original goal each handoff]]

Agent 3: [[Reviewer/QA]]

Role: [[Checks the draft against the original requirements before it reaches a human]]

Input: Agent 2's output + original brief

Output: [[Pass/fail + specific fix list, or approved final output]]

Human checkpoint: [[Where a person reviews before this ships: e.g. before sending externally]]

Orchestration Notes

• Pass structured data between agents (JSON or clearly labeled sections), not free-form prose: it reduces information loss at each handoff.
• Re-state the original goal to every agent in the chain, not just the first one: long chains drift from the brief.
• Cap the chain length. Beyond 4–5 agents, errors compound faster than quality improves.

Worked Examples

Example 1: Content Pipeline

Goal: Weekly blog post from a topic idea.

Chain: Researcher (gathers stats + sources) → Outliner (structures sections) → Writer (drafts) → Editor (checks against style guide + fact-checks stats) → Human final review before publish.

Example 2: Customer Support Triage

Goal: Route and draft responses to support tickets.

Chain: Classifier (urgency + category) → Knowledge-base retriever (pulls relevant docs) → Response drafter (writes reply) → Human approves before sending for anything flagged high-urgency.

Common Mistakes to Avoid

• Too many agents for a task that one well-prompted agent could handle: added complexity without added quality
• No human checkpoint anywhere in a chain that touches real customers or money
• Free-form handoffs between agents instead of structured data, causing details to get lost
• No failure-mode guardrails: one agent's hallucination flows silently into the next agent's output
• Not re-stating the original goal at each stage, letting the chain drift from the actual brief

Start with 2 agents and a human checkpoint. Add complexity only once you've proven the simple version works.