ARTICLE AD BOX
I’m building an AI-driven workflow platform using TypeScript, Next.js, Node.js, and GitHub-integrated deployment pipelines. The system coordinates multiple autonomous agents that handle orchestration, API actions, validation layers, and async task execution.
Current architecture includes:
Next.js frontend
Node.js backend services
GitHub-connected CI/CD
Webhook/event-driven workflows
AI agent task routing
API validation + retry logic
Fintech-oriented security requirements
I’m trying to determine best practices for:
Preventing cascading failures between autonomous agents
Structuring agent-to-agent communication
Managing retries/idempotency for webhook events
Logging and observability across distributed workflows
Safely deploying iterative AI workflow updates to production
For developers who have worked on production AI orchestration systems:
What architectural patterns worked best?
Did you use queues/event buses/service meshes?
How did you handle state management and rollback strategies?
Would appreciate examples, frameworks, or lessons learned from scaling similar systems.
