Project 15: Multi-Agent Command Mesh (Roles, Delegation, Consensus)

Build a supervisor-led team of specialized agents with robust delegation, messaging contracts, consensus, and state synchronization.

Quick Reference

Attribute Value
Difficulty Level 5: Master
Time Estimate 35-60 hours
Main Programming Language TypeScript
Alternative Programming Languages Python, Go
Coolness Level Level 5: Pure Magic
Business Potential 4. The “Open Core” Infrastructure
Prerequisites distributed systems basics, orchestration patterns
Key Topics role specialization, supervisor patterns, consensus, synchronization

1. Learning Objectives

  1. Design explicit role contracts for specialist agents.
  2. Implement delegation with ownership boundaries.
  3. Add consensus/voting and conflict arbitration.
  4. Synchronize shared state across parallel branches.
  5. Recover from partial multi-agent failure gracefully.

2. Theoretical Foundation

2.1 Why Multi-Agent Systems

A single LLM persona can become overloaded by complex tasks requiring diverse expertise. Multi-agent design decomposes work into roles: planner, researcher, critic, executor, and synthesizer. This reduces cognitive interference and supports parallelism. The trade-off is coordination overhead, which you must control with strict protocols.

2.2 Coordination Mechanics

Message schemas, versioned state, and arbitration rules are core. If these are ambiguous, teams become non-deterministic and hard to debug. Your architecture must define who owns decisions, when to escalate, and how conflicts are resolved.

3. Project Specification

3.1 What You Will Build

A command mesh runtime with:

  • supervisor process
  • role agents
  • message bus
  • shared state store
  • consensus/arbitration module
  • observability panel

3.2 Functional Requirements

  1. Route requests from supervisor to specialist roles.
  2. Support both sequential and parallel orchestration modes.
  3. Run voting-based decision merge for conflicting proposals.
  4. Maintain state versioning and conflict logs.
  5. Escalate unresolved conflicts to human review channel.

3.3 Non-Functional Requirements

  • Reliability: recover from role crashes.
  • Traceability: event timeline for every delegation.
  • Determinism: reproducible replay mode.

3.4 Real World Outcome

$ mesh run "Recommend SOC2-compliant AI hosting vendor"
[Supervisor] roles=researcher,cost,compliance,critic
[Parallel] tasks dispatched=4
[Consensus] proposal-A votes=2 proposal-B votes=1 abstain=1
[Arbitration] missing evidence on compliance control -> task reopened
[State] version=47 merged
[Final] vendor=Vendor-B confidence=0.76

4. Solution Architecture

4.1 High-Level Design

User Intent -> Supervisor
               /   |   \
          Research Cost Compliance
               \   |   /
                Critic
                  |
            Consensus Engine
                  |
             Final Output

4.2 Key Components

Component Responsibility Key Decisions
Supervisor task decomposition + routing role assignment policy
Role agents specialized analysis strict input/output schemas
Consensus engine merge proposals weighted voting + tie-breaker
State store synchronize context optimistic concurrency + merge logs

5. Implementation Guide

5.1 The Core Question You’re Answering

“How do specialist agents collaborate effectively without deadlocks, conflicts, or opaque behavior?”

5.2 Concepts You Must Understand First

  1. Actor model fundamentals
  2. Delegation and ownership semantics
  3. Consensus and arbitration patterns
  4. Concurrent state merge strategies

5.3 Questions to Guide Your Design

  1. Which role can make final decisions?
  2. How do you detect delegation cycles?
  3. When is consensus required versus optional?

5.4 Thinking Exercise

Model a disagreement where compliance and cost agents conflict. Define merge rules and escalation thresholds.

5.5 The Interview Questions They’ll Ask

  1. Why multi-agent instead of one generalist?
  2. How do you avoid role ping-pong loops?
  3. How do you version message schemas?
  4. How do you recover from one failed agent?
  5. How do you audit decision quality across agents?

5.6 Hints in Layers

Hint 1: Start with three roles only.

Hint 2: Add strict schema validation before enabling parallel mode.

Hint 3: Introduce consensus only for high-impact choices.

Hint 4: Keep one global monotonic state version.

5.7 Books That Will Help

Topic Book Chapter
Distributed coordination “Designing Data-Intensive Applications” Ch. 8-9
Architecture decisions “Fundamentals of Software Architecture” Communication patterns
Agent patterns “Building AI Agents” multi-agent chapters

5.8 Common Pitfalls and Debugging

Problem 1: delegation cycles

  • Why: no cycle checks.
  • Fix: maintain visited-role chain and max-depth.
  • Quick test: synthetic cyclic task exits with explicit error.

Problem 2: state corruption in parallel branches

  • Why: conflicting writes merged silently.
  • Fix: semantic merge + conflict queue.
  • Quick test: concurrent updates produce conflict report.

5.9 Definition of Done

  • Multi-role execution works in both sequential and parallel modes
  • Consensus and arbitration are explicit and logged
  • State synchronization survives concurrent writes
  • Recovery path exists for partial role failure