Building Agentic AI Systems

The shift from prompt-and-response AI to agentic systems represents a fundamental change in how we build software. Instead of generating text, agents reason about goals, decompose tasks, and execute multi-step plans autonomously.

What makes an agent "agentic"

An agentic AI system has three core capabilities:

1. Planning — breaking a high-level goal into concrete steps
2. Tool use — calling APIs, reading files, running code, interacting with external systems
3. Reflection — evaluating its own outputs and adjusting course

The gap between a chatbot and an agent is the feedback loop. A chatbot generates a response and stops. An agent generates an action, observes the result, and decides what to do next.

Architecture patterns

Most production agent architectures follow a similar pattern:

while not done:
    observation = perceive(environment)
    plan = reason(observation, goal, memory)
    action = select_action(plan)
    result = execute(action)
    memory.update(result)
    done = evaluate(result, goal)

The details matter — how you manage context windows, handle tool failures, implement guardrails, and prevent infinite loops. But the core loop is consistent.

Lessons learned

After shipping several agentic systems, a few patterns have become clear:

• Deterministic scaffolding around non-deterministic models: Use structured outputs, validation layers, and retry logic to make the overall system reliable even when individual LLM calls are unpredictable.
• Observability is non-negotiable: Every agent action, tool call, and decision point needs to be logged and traceable. When an agent does something unexpected in production, you need to understand why.
• Start with single-agent, scale to multi-agent: Multi-agent orchestration adds complexity. Most problems are better solved with a well-designed single agent before introducing coordination overhead.

The field is moving fast. What's clear is that building with agents requires a different mental model than traditional software engineering — one that embraces uncertainty while building robust systems around it.