Multi-Agent Autonomous Orchestration

Automations built on a single LLM prompt hit a ceiling quickly: the generalist model loses precision in specialized domains (finance, support, engineering). Multi-agent orchestration solves this by delegating subtasks to agents with dedicated context and tools.

The core pattern is classify → delegate → consolidate:

1. The orchestrator receives the user request.
2. Classifies intent (intent routing) with a short prompt or fine-tuned classifier.
3. Triggers only the sub-agents needed — in parallel when possible.
4. Aggregates partial responses into one coherent final answer.

This approach reduces wasted tokens and allows different SLAs per specialist (e.g. billing agent with 5s timeout, research agent with 30s).

In this reference, the orchestrator agent Tyna acts as the single entry point. It maintains conversation history, decides which specialists to invoke, and validates the consolidated response before returning it to the user.

Typical components:

• Orchestrator (Tyna) — LLM with routing system prompt + JSON delegation schema.
• Sub-agents — isolated Make/n8n workflows, each with tools limited to its domain.
• Event bus — HTTP webhooks with a standardized payload ({ task_id, intent, context, deadline_ms }).
• Consolidator — final node that merges partials, removes redundancy, and formats the response.

Isolating sub-agents behind Make webhooks lets you scale, version, and test each specialist independently — without redeploying the orchestrator.

Delegation via Make webhooks works as follows:

1. Tyna sends a POST to /hooks/make/agent-{specialty} with serialized context.
2. Each sub-agent runs its workflow (API calls, local RAG, calculations) and returns structured JSON.
3. The orchestrator waits for responses with Promise.allSettled — partial failures do not crash the entire request.
4. A consolidation prompt receives the partials and produces the final answer.

Best practices:

• Per-sub-agent timeout — 10–15s default; cancel slow requests.
• Idempotency key — task_id prevents duplication on retries.
• Response schema — enforce JSON with status, data, confidence fields to filter low-quality partials.
• Fallback — if no sub-agent responds, Tyna replies with reduced scope or escalates to a human.