Building Production AI Agents: Architecture, Tools, and Guardrails

Strip away the hype and an AI agent is a program that uses an LLM as its reasoning engine to decide what actions to take. It has a goal, a set of tools it can use, and a loop: observe the current state, think about what to do next, take an action, observe the result, repeat until the goal is achieved.

This is fundamentally different from a chatbot, which maps inputs to outputs in a single step. An agent might need 5, 10, or 50 steps to complete a task — looking up customer records, checking inventory, calculating discounts, drafting a response, and scheduling a follow-up — all from a single customer request.

The magic is that the LLM handles the planning and reasoning, while deterministic code handles the actual execution. The LLM decides "I need to check the order status" and generates a tool call; your code executes the actual API call and returns the result. This separation is critical for reliability.

There are three main patterns for building AI agents, each suited to different use cases:

ReAct (Reasoning + Acting)

The simplest and most common pattern. The agent alternates between thinking (reasoning about what to do) and acting (calling a tool). Each thought-action cycle is one step. This works well for straightforward tasks like customer support, data lookup, and form filling.

Plan-then-execute

The agent first creates a complete plan, then executes each step. This works better for complex tasks where the order of operations matters — like generating a report that requires data from multiple sources, or processing an insurance claim that has specific procedural requirements.

Multi-agent collaboration

Multiple specialized agents work together on complex tasks. A "manager" agent coordinates, delegating subtasks to specialist agents (research agent, writing agent, code agent). This is powerful for tasks that require different types of expertise, but adds complexity in coordination and error handling.

Tools are what give agents their power. A customer support agent without tools is just a chatbot. With tools — order lookup, refund processing, shipping tracking, knowledge base search — it becomes a system that can actually resolve issues.

Each tool needs: a clear description (so the LLM knows when to use it), well-defined parameters (so it can construct valid calls), error handling (so failures don't crash the agent), and rate limiting (so a confused agent doesn't call an API 1000 times).

We define tools as simple functions with TypeScript types. The type definitions become the tool descriptions the LLM uses to understand each tool's purpose and parameters. This keeps tool definitions in sync with their implementations — when you change the code, the LLM's understanding updates automatically.

Start with 3-5 tools and add more as you validate the agent works. An agent with 50 tools will be confused; an agent with 5 well-designed tools will be effective.

Agents need memory to be useful beyond single interactions. There are three types:

Short-term memory: the current conversation context. This is the LLM's context window. For long conversations, you need to summarize older messages to stay within token limits.

Working memory: information the agent needs during a multi-step task. "The customer's order number is #12345, their shipping address is in Mumbai, and they want a refund for the damaged item." This persists across tool calls within a single task.

Long-term memory: information that persists across sessions. Customer preferences, previous interactions, learned patterns. We implement this with a vector database (like Pinecone or pgvector) that the agent can search when relevant context is needed.

The key insight is that memory should be structured, not just a dump of conversation history. Storing "Customer prefers email communication" is more useful than storing the full conversation where they mentioned it.

Production agents need guardrails. Without them, a confused agent can send wrong emails, process incorrect refunds, or expose sensitive data. Here's what we implement:

Input guardrails: validate and sanitize all user inputs before they reach the agent. Block prompt injection attempts. Rate-limit requests per user.

Output guardrails: validate all agent outputs before they're executed. An agent should never be able to send an email, process a payment, or modify data without the output passing through validation rules.

Action guardrails: limit what tools an agent can call and with what parameters. A customer support agent shouldn't be able to issue refunds over $500 without human approval. A research agent shouldn't be able to access HR records.

Observability: log every thought, action, and tool call. Build dashboards that show agent performance, error rates, and escalation patterns. You should be able to replay any agent session to understand what happened and why.

The goal is defense in depth — multiple layers of protection so that no single failure can cause real harm.