Agentic AI Architecture: Building Production-Grade AI Agents

Agentic AI is the most significant shift in how we build software since the internet. Instead of deterministic code that follows a fixed path, agents perceive their environment, reason about it, and take actions to achieve goals.

But building agents that work reliably in production is hard. Here's the architecture that actually works.

What Makes an Agent "Agentic"

A true AI agent has four capabilities:

The key insight: agents are not just prompts. They are systems.

Core Architecture Patterns

Pattern 1: ReAct (Reason + Act)

The foundational pattern. The agent alternates between reasoning about what to do and taking an action:

```

Thought: I need to find the customer's order history

Action: query_database(customer_id="C123")

Observation: [order history data]

Thought: Now I can answer the question

Answer: The customer has placed 5 orders...

```

Pattern 2: Plan-and-Execute

For complex, multi-step tasks. First plan all steps, then execute them:

Pattern 3: Multi-Agent Orchestration

Multiple specialised agents, each with a defined role, coordinated by an orchestrator:

The Reliability Problem

The biggest challenge with agents: they can go wrong in unpredictable ways. Key principles for reliable agents:

1. Constrain the action space

Don't give agents more tools than they need. Every tool is a potential failure point.

2. Add checkpoints

For long workflows, add human-in-the-loop checkpoints at high-stakes decisions.

3. Implement timeouts

Agents can loop. Always set maximum step counts and timeouts.

4. Log everything

Every LLM call, every tool use, every decision. You cannot debug what you cannot see.

5. Test with adversarial inputs

Agents will encounter unexpected inputs. Test for edge cases explicitly.

Memory Architecture

Agents need different types of memory:

The Right Stack in 2026

Building agents is complex. Talk to our team about how we architect production-grade AI agents.