Building AI Agents in Production: Parcha's Lessons on Async Architecture, Multi-Agent Coordination, and Error Recovery
Parcha's initial naive approach to building AI agents for compliance and KYB workflows suffered from WebSocket reliability issues, context window overload from single-agent SOPs, no error recovery mechanisms, LLM hallucinations causing tool failures, and tightly coupled agent implementations that could not be reused.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Agent triggered via API or Slack
Agents can be triggered through an API, followed through a Slack channel, or evaluated at scale as headless processes.
Tools used
Langchain AgentsRedisRQOCR
Outcome
Parcha rebuilt their agent architecture with async long-running tasks via pub/sub, a coordinator/worker multi-agent model to contain context windows, Redis-based shared memory, well-typed exception handling for agent self-correction, and reusable composable tool building blocks, significantly reducing catastrophic failures.
What failed first
Parcha's initial build used WebSocket connections for bi-directional communication, a single agent with the full SOP embedded in its scratchpad, the scratchpad alone for memory, and no failover or exception handling, causing the agent to fail unrecoverably on any step error.