Multi-Agent — Orchestrate Specialized AI Workers
Key Points
- ✓Spawn specialized agents per task
- ✓Agent-to-agent communication
- ✓Shared context when needed
- ✓Parallel task execution
- ✓Automatic result aggregation
- ✓Resource-aware scheduling
How It Works
- 1Define agent roles in config or on-the-fly
- 2Main agent delegates tasks to specialists
- 3Agents work in parallel with isolated contexts
- 4Results flow back to the orchestrator automatically
Real-World Use Cases
Orchestrator + Research Workers
The main agent breaks a research task into subtopics and spawns a specialist for each. One agent digs into technical docs, another scans forums for real-world experiences, a third checks recent papers. All three report back; the orchestrator synthesizes the final analysis.
kimi + minimax Parallel Reasoning
Use kimi for long-context document analysis and minimax for structured reasoning in parallel. The orchestrator routes the right content to the right model and merges outputs — as seen in production Discord deployments by Hermes users.
Code Review Pipeline
Spawn a security agent, a style agent, and a logic agent on the same PR simultaneously. Each reviews from its specialty perspective, and the orchestrator produces a unified review with non-overlapping concerns flagged separately.
Automated Customer Support Routing
Route incoming support requests to specialist agents by topic — billing to one agent, technical issues to another, feature requests to a third. Each has domain-specific skills loaded; the orchestrator handles escalation logic.
Under the Hood
Hermes multi-agent orchestration implements a hierarchical task decomposition model. The orchestrator (main agent) analyzes a complex task, identifies the optimal work breakdown structure, and spawns specialist worker agents with tailored context: each worker receives only the task-relevant subset of the full conversation context plus any shared skills or knowledge the orchestrator decides to pass. This selective context sharing prevents workers from being confused by irrelevant background while keeping coordination overhead low.
Agent-to-agent communication flows through a structured message passing layer — workers don't share memory directly, they exchange typed result objects that the orchestrator can validate, transform, and route. This prevents the 'telephone game' degradation that plagues naive multi-agent setups where agents summarize each other's summaries. The orchestrator receives raw structured outputs and does the synthesis itself, maintaining full fidelity.
Parallel execution is first-class: the orchestrator can fire multiple worker agents simultaneously and wait for all results before proceeding, or use streaming results to start synthesis as early outputs arrive. Resource-aware scheduling prevents over-spawning — configurable concurrency limits ensure you don't accidentally blow your API rate limits or exhaust your VPS memory with a runaway agent fleet. The architecture is directly inspired by real production patterns shared in the Hermes Discord community, including kimi+minimax parallel configurations and orchestrator+specialist deployments.