AI Agent Workflow
AI / MLAdvancedAgentic automationAutonomous AI agent with LLM inference, RAG retrieval, tool execution, async job queue for long tasks, human approval checkpoint, and persistent memory. Use for copilots, autonomous workflows, and AI agents.
Recommended for: Teams orchestrating tool-using AI agents
Use Case
AI copilots, autonomous research agents, customer support bots, code assistants
Best Fit Scenarios
- Workflow copilots with approval checkpoints
- Automation requiring retrieval plus tool execution
- Agent products that need persistent memory and audit trails
Stack Breakdown
Architecture Layers
Components by Category
frontend
backend
external
database
async
Why This Topology Works
The orchestrator runs an agent loop: prompt → LLM → tool dispatch → response. Vector DB enables RAG retrieval for grounded answers. PostgreSQL persists conversation memory across sessions.
Scaling Notes
Orchestrator is stateless and scales horizontally. LLM calls are the bottleneck — use request queuing and model routing. Vector DB indexes scale with embedding partitioning.
Observability
Track tokens per request, retrieval relevance scores, tool call success rates, and end-to-end latency from prompt to response.
Typical Bottlenecks
- Frontend rendering and bundle delivery under peak traffic
- Service latency and timeout behavior on critical routes
- Third-party SLA variability and quota exhaustion
Async Flow and Reliability
User-facing operations remain synchronous while long-running work moves through queues or streams. Workers consume jobs independently with retry and failure isolation, improving resilience under burst load.
Upgrade Path
Harden each domain with clear ownership, enforce SLO budgets, and adopt multi-region or active-passive failover where downtime costs are high.
Operating Envelope
Complexity is marked as Advanced with an intended scope of Agentic automation. Use this as a planning baseline before adapting the template to your reliability and team constraints.