Agentic Engineering - Multi-Agent Systems in Production
Building autonomous AI systems that reason, plan, and execute in regulated environments.
Agentic engineering is the practice of designing and deploying AI systems that can perceive their environment, reason about goals, plan multi-step actions, use tools, and execute autonomously - all while remaining safe, observable, and controllable.
At Fitch Ratings, I build agentic systems that enable analysts to interact with credit ratings data through natural language. These systems combine retrieval-augmented generation, multi-agent orchestration, and human-in-the-loop checkpoints to deliver reliable intelligence in a regulated environment.
Core Patterns
- Multi-Agent Orchestration: Specialist agents collaborate - one retrieves, one analyzes, one validates
- State Machines: LangGraph provides the graph-based execution model for complex workflows
- Human-in-the-Loop: Critical decisions require analyst approval before execution
- Observability: Every agent action is logged, traced, and auditable
Why Agentic Engineering Matters
The difference between a chatbot and an agentic system is autonomy and reliability. Agentic systems don't just answer questions - they execute workflows, make decisions within guardrails, and escalate when uncertain. This is the next frontier of production AI.