The Supervisor Pattern: Boss and Worker Agents
In multi-agent systems, a supervisor coordinates specialized workers: research, coding, verification, and customer comms. The supervisor’s job is planning, delegation, and convergence—not doing every subtask itself.
Define explicit handoff formats (JSON schemas preferred) so agents do not rely on fragile natural-language state.
Handling Hallucinations: Cross-Verification Loops
Add critic or checker steps for high-risk outputs: compare against tool results, re-query sources, or require dual agreement before writes. For numerical claims, route through calculators or databases—not prose reasoning alone.
Communication Protocols: JSON vs. Natural Language
Natural language is flexible but ambiguous. For production, prefer structured messages with typed fields, versioned schemas, and idempotency keys for actions. Reserve freeform dialogue for exploration and debugging.
The Bottleneck Problem: Latency in Agent Chains
Each hop adds tokens and waiting. Mitigate with:
- Parallel fan-out where tasks are independent.
- Caching of retrieval and tool reads.
- Early stopping when confidence thresholds are met.
- Streaming UX so users see partial progress.
Operational Oversight: Dashboarding a Digital Workforce
Instrument per-agent success rates, average hops-to-resolution, tool error codes, and cost per task. Alert on drift: sudden spikes in retries, new error classes, or declining user acceptance.
Run weekly postmortems on failed traces with redacted transcripts—treat agents like services, not magic.
FAQ
When is multi-agent overkill?
If a single model with tools solves 90% of cases, extra agents add complexity without benefit.
How do we test orchestration?
Golden-path tests plus fuzzed inputs for tool payloads; replay production traces in staging.
What about vendor lock-in?
Abstract transport and schemas; keep business rules outside vendor-specific SDKs.
Continue reading on the AI Hub or reach out via contact for production agent design.