Why single-agent copilots break in production
Multi-agent systems in 60 seconds
Multi-agent systems architecture
Agent communication protocols and coordination patterns
Walkthrough: the support copilot end to end
Logging & evaluation hooks (observability)
Where MAS fits and where it’s a bad idea
A single-agent copilot can look great in a demo and still break in production. It loops, calls tools out of order, “assumes” data it never fetched, or answers confidently when it should escalate. The cost shows up as rework, slower resolution, and higher risk in customer-facing flows.
Multi-agent systems or MAS fix this by turning one overloaded brain into a coordinated team: specialized agents, shared state, strict tool access, and clear protocols.
Below, I’ll explain how multi-agent systems work using one running example: a SaaS customer support copilot that pulls CRM context, searches docs and past tickets, drafts a policy-safe reply, and escalates when uncertainty or risk is high.
Single agents fail when the workflow needs multiple skills and checks at once.
→ Loops: no clear “done” state
→ Tool chaos: repeated calls, wrong order
→ Unsafe outputs: ungrounded claims, policy drift, implied actions
If your flow needs triage, retrieval, account context, policy checks, and escalation, you’re already doing teamwork. MAS just makes it controllable.
A multi-agent system is multiple AI agents working toward a shared goal. Each agent has one role, limited tools, and a shared place to store evidence and decisions.
→ Sense: read the ticket and shared state
→ Think: choose the next step
→ Act: call a tool or produce an output
→ Share: write results back to shared state
A reliable multi-agent systems architecture is less about “more agents” and more about boundaries and guardrails.
→ Triage agent: detect intent, priority, missing fields
→ CRM agent: pull plan/tier, billing status, invoices (read-only)
→ Retrieval agent: search docs + past tickets + known issues
→ Drafting agent: write the reply
→ Policy/tone agent: check compliance and overpromises
→ Escalation agent: decide send vs human handoff
→ Orchestration: one orchestrator calls agents in sequence (best for MVPs)
→ Choreography: agents signal each other directly (harder to debug)
Start orchestrated. Move to partial choreography only after you can observe failures.
Typical tools for the support copilot:
→ CRM lookup (read-only)
→ knowledge base search
→ ticket history fetch
→ escalation create-task
→ send-reply (often gated)
Rule: if the response depends on truth (policy text, invoice status, account tier), it must come from a tool result, not “model memory.”
You usually need:
→ short-term state: current ticket + working notes
→ long-term storage: durable customer history (permissioned)
→ shared state: the blackboard every agent reads/writes
→ Permissions: which agent can access which tools/data
→ Budgets: max tool calls, tokens, time per ticket
→ Schemas: required fields before “send”
→ Fallbacks: ask one question or escalate when evidence is weak
Protocols are the rules that stop duplication, disagreement, and drift.
A handoff should specify:
→ input: what the next agent receives
→ output: the schema it must return
→ done: what completion means
Example: triage hands off intent=billing_duplicate_charge, customer_id, and missing_fields=[invoice_id] to CRM + retrieval.
→ Orchestrator posts the task
→ Agents respond with confidence + tool needs
→ Orchestrator assigns the best fit
→ Drafting agent writes
→ Policy/tone agent reviews for grounding + policy + tone
→ One round of targeted edits (avoid endless rewrites)
→ received → triaged → context_fetched → evidence_collected → drafted → reviewed → sent_or_escalated
User: “I was charged twice. Fix it.”
→ detect intent + urgency
→ capture missing fields (invoice_id if absent)
→ write plan into shared state
→ CRM agent pulls invoice/billing status
→ Retrieval agent pulls policy and known-issue guidance
→ Ticket history agent fetches relevant prior cases (if allowed)
→ Drafting agent writes a reply grounded in evidence
→ Policy/tone agent checks compliance and tone
→ If evidence is sufficient, send
→ If evidence is missing/contradictory or risk is high, escalate with a human-ready summary
→ tool calls and outcomes
→ evidence used in the final reply
→ escalation reasons
→ outcome signals (reopens, satisfaction)
→ support and ops workflows with tools & policies
→ incident response copilots with runbooks & escalation
→ research + synthesis where retrieval and verification matter
→ simple chat with no tools (single agent is cheaper)
→ workflows without ground truth (too much guessing)
→ high-stakes decisions without human review and audit trail
→ 2–3 agents max to start (triage, context, draft+review)
→ orchestration first
→ strict tool permissions
→ schemas everywhere
→ budgets (time, tokens, tool calls)
→ state machine + structured outputs
→ logging + eval set
→ safe fallbacks (ask once or escalate)
Use MAS when the job splits into specialist steps that require tools and verification. If you can’t define each agent’s role and success criteria, keep it single-agent.
If you’re considering a production multi-agent system for support or ops, an MAS architecture review will surface likely failure points before engineering time gets burned.
A MAS is a group of specialized AI agents that share state and coordinate work to complete a task. Each agent has a clear role (triage, retrieval, drafting, review, escalation) and limited tools, so the system is more reliable than one “do-everything” agent.
