Multi-Agent Coordination Protocol

Rules for role-based agent teams: brief-only messages, independent answers first, round and depth caps, loop detection, one synthesis.

AI Systemsmulti-agentagent-orchestration
by AgentVoltv1.0.0
Free to sign up · every skill included with AgentVolt Pro

Before & after

Same prompt, same agent — the only difference is the skill.

Prompt“Set up multiple agents to coordinate a cross-functional board meeting analysis”
Beforegeneric AI agent
Running multiple agents requires coordination to avoid conflicts and ensure they work together effectively. You need to define what each agent's role is and how they communicate with each other. Consider creating a shared context that all agents can access. Use standardized message formats so agents understand each other. Set up error handling for when an agent fails or gives conflicting advice. You might want to run agents sequentially or in parallel depending on the task. Testing with a few scenarios helps ensure the setup works correctly.
Afterwith Multi-Agent Coordination Protocol
AGENT PROTOCOL SETUP: C-SUITE BOARD MEETING

COMPOSITION
Board meeting analysis: 3 agents
  • CFO Agent (Financial analysis, modeling, risk)
  • COO Agent (Operations, execution, timeline)
  • CMO Agent (Go-to-market, revenue, brand)

INVOCATION SYNTAX

Step 1: Initialize multi-agent session
  /board-sync --agenda "Q3 spending strategy" --date 2026-08-15
  Triggers CFO, COO, CMO sequentially

Step 2: Each agent receives context block
  agent_id: cfo-001
  task: Analyze proposed Q3 budget of $2M, assess ROI assumptions
  constraints: Max response time 5 min, JSON output, refer to CMO output

Step 3: Agent loops execute in order
  Loop 1 - CFO first (financial foundation)
    Input: Business context, Q3 targets
    Output: Budget allocation, risk flags, assumptions
  
  Loop 2 - COO after CFO
    Input: CFO output, operational constraints
    Output: Execution plan, resource needs, bottlenecks
  
  Loop 3 - CMO after COO
    Input: CFO budget, COO timeline
    Output: Go-to-market plan, revenue model, risks

LOOP PREVENTION
  Isolation rule: Agents read prior outputs but cannot re-invoke agents
  Circular dependency check: If CMO asks for COO re-run, request rejected
  Escalation: Human decides trade-offs

RESPONSE FORMAT (All agents follow)

json
{
  agent_id: cfo-001,
  timestamp: 2026-08-15T10:30:00Z,
  task: Analyze Q3 budget and ROI,
  recommendation: Approve $2M with conditions...,
  assumptions_made: [Revenue 15% YoY, CAC stable $120],
  dependencies_on_other_agents: [{agent: CMO, field: revenue_target}],
  conflict_flags: [{agent: COO, issue: Timeline conflict}],
  next_steps: [Human review, CMO can proceed]
}

CONFLICT RESOLUTION

Scenario A: CFO and CMO disagree on CAC budget
  Identify in JSON conflict_flags
  Capture both positions
  Flag for human decision
  Provide trade-off analysis

Scenario B: COO timeline infeasible for CMO
  CFO notes dependency
  CMO flags impossibility
  Protocol stops; human resolves
  Options: Extend timeline, increase resources, reduce scope

ERROR HANDLING
  • Agent timeout (>5 min): Return partial analysis + flag
  • Missing dependency: Retry once, escalate
  • Conflicting facts: Log both, flag for human review

OUTPUT STRUCTURE (After all agents complete)
  1. Executive summary (recommendation + key trade-offs)
  2. Agent analyses (CFO, COO, CMO sections)
  3. Conflict resolution (if any disputes)
  4. Next steps (decision needed, follow-up actions)
  5. Audit trail (who said what, when, assumptions)

About this skill

Make a team of role agents, like CFO, CTO, and CMO advisors or specialist subagents, produce a better answer than one agent would, without looping or all agreeing. Or find out that one agent is the better choice.

What it does

  • Tests whether the job really needs multiple agents before building it
  • Defines non-overlapping role scopes and a fixed request and response format
  • Collects independent answers before any sharing, then one cross-examination round
  • Caps depth and rounds, routes through one orchestrator, and detects loops
  • Keeps disagreements visible and ends with one recommendation and its deciding assumption

What you get

Either a run of the session with a clear synthesis, or a protocol spec you can implement in your agent framework, plus a failure-mode table for debugging.

Best for

Builders of agent systems and anyone running advisor-panel or board-meeting style sessions with AI roles.

SKILL.md preview

SKILL.md
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name: agent-protocol

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