Agentic AI Platform

Problem and constraints

LLM workflows lacked grounding, guardrails, and the same ops visibility teams expect from production services.

Role and ownership

Designed and built orchestration, RAG evaluation, and telemetry integration as a reference architecture for production-minded AI workflows.

Key engineering decisions

• Treat agents as multi-step workflows with tool routing and human-in-the-loop checkpoints
• Ground answers with RAG and evaluation harnesses instead of unconstrained prompts
• Export latency and cost into existing Prometheus/Grafana stacks rather than a separate AI-only console
• Deterministic CI evaluation gates, a model allowlist, token caps and a kill switch for model calls

Tradeoffs and alternatives

• Human checkpoints improve reliability at the cost of full automation speed
• This is a reference architecture and demonstration path - not a claim of a large public SaaS deployment

Reliability, deployment, and observability

Guardrails, evaluation, and shared observability make failure modes visible the same way other services are operated.

Results and lessons

• Reliable multi-step completions with explicit tool boundaries
• Latency and cost tracked alongside platform telemetry for production visibility

Technologies