1. The Core Pattern: Reasoning + Acting (ReAct)

At the heart of every reliable agent lies a specific cognitive pattern known as ReAct (Reason + Act). This is not just a prompt trick; it is an architectural necessity.

Standard prompting asks the LLM to answer immediately. This forces the model to "guess" the final answer without verifying facts. ReAct forces the model to stop and think before it speaks.

This distinction is critical: In a traditional script, error handling is explicit (`if error: retry`). In an agent, error handling is often semantic. If a tool fails, the agent receives the error message as an "Observation" and must reason its way out of the failure state.

2. Tool Orchestration: Giving the Agent Hands

An LLM without tools is just a librarian who has read every book but cannot leave the library. To make an agent useful, you must define its Tool Schema rigorously.

The biggest mistake developers make is providing vague tool descriptions. If you tell an agent it has a "search tool," it will hallucinate parameters. Instead, define strict JSON schemas.

{
  "name": "web_search",
  "description": "Searches Google for current events. Returns top 5 results.",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {"type": "string", "description": "The search query"},
      "limit": {"type": "integer", "default": 5}
    },
    "required": ["query"]
  }
}

3. Memory Architectures: Context vs. State

One of the most confusing aspects of agent design is memory. There are two distinct types you must manage, and confusing them leads to bloated token costs and confused agents.

4. Guardrails and Observability

In production, trust is the currency. You cannot ship an agent that might delete a database or send an email to the wrong person because it misunderstood a prompt.

You need a Guardrail Layer. This is code that runs outside the LLM to validate its intentions before execution.

• Pre-flight Checks: Validate parameters before sending them to an API (e.g., ensure an email address is valid).
• Human-in-the-Loop: For high-stakes actions (sending money, deploying code), pause the agent and require a human click-to-approve.
• Output Filtering: Scan the agent's generated text for PII or toxic content before showing it to the user.

Observability isn't just logging tokens. It's tracing the decision tree. You need to know why the agent chose Tool A over Tool B.

5. Implementation Checklist

Before deploying your agent to production, run it against this checklist. If you can't check these boxes, you aren't ready.

Frequently Asked Questions