2026 · Architectural
Multi-Agent Orchestration Platform
Designed and built a platform for orchestrating multiple specialized AI agents across complex workflows — handling task decomposition, inter-agent communication, and result aggregation.
agents orchestration python
Overview
Built an orchestration layer for a startup deploying multiple specialized agents (research, coding, review) that needed to collaborate on complex tasks. The system handles task decomposition, parallel execution, inter-agent messaging, and result synthesis.
Key Challenges
- Task decomposition — breaking complex user requests into subtasks that map to specific agent capabilities
- State management — tracking the state of each agent and the overall workflow without losing context
- Error propagation — handling failures in one agent without crashing the entire pipeline
- Cost control — preventing runaway token usage across multiple concurrent agents
Architecture
The platform uses an event-driven architecture built on:
- Task queue (Redis Streams) for distributing work to agent workers
- State store (PostgreSQL) for persistent workflow state
- Message bus (NATS) for inter-agent communication
- Observability (OpenTelemetry) for distributed tracing across agents
Each agent runs as an independent worker process, pulling tasks from the queue and publishing results back. The orchestrator manages the workflow DAG and handles retries, timeouts, and fallbacks.
Results
- 40% reduction in end-to-end task completion time through parallel execution
- 99.5% workflow completion rate with graceful degradation
- Token costs reduced by 30% through intelligent context management