Building Custom AI Agents from Scratch: A Practical Guide

Stop using ChatGPT wrappers and calling them "AI agents." A text box that sends prompts to an API is a chatbot. An agent acts. It reads data, makes decisions, uses tools, and produces results without someone babysitting every step.

Building a real agent requires understanding four fundamental components: perception, reasoning, action, and memory. Get any one of these wrong and your "agent" is just an expensive autocomplete.

I've built agents that review code, agents that manage deployments, agents that write documentation, and agents that coordinate other agents. The principles are the same regardless of the use case. Here's how to build one that actually works.

Start with the Mission

The most important decision you make is what your agent does NOT do.

A general-purpose agent that tries to handle everything is a general-purpose agent that handles nothing well. The context window gets stuffed with tools it rarely uses. The system prompt becomes a novel. The model spends more time figuring out which tool to use than actually using it.

Specialized agents dominate. An agent that only reviews Python code for security vulnerabilities will outperform an agent that reviews code, writes documentation, manages tickets, and sends emails. Every time.

Define the mission in one sentence. "This agent reviews pull requests for security vulnerabilities and suggests fixes." If you can't state the mission in one sentence, your agent is too broad.

Define the boundaries explicitly. "This agent does NOT merge pull requests, does NOT modify code directly, and does NOT interact with users." Boundaries prevent scope creep and reduce the chance of unintended actions.

The Reasoning Engine

For most agents, the reasoning engine is a large language model with a system prompt. The system prompt is the most undervalued component in the entire stack.

A bad system prompt: "You are a helpful AI assistant that reviews code."

A good system prompt defines the agent's identity, its decision-making framework, its constraints, and its output format. It tells the model not just what to do, but how to think about what to do.

Here's what a production system prompt includes:

Identity: Who the agent is and what it does. One paragraph.

Decision framework: The criteria the agent uses to evaluate situations and choose actions. For a code review agent, this includes severity classifications, which patterns are always flagged, which are context-dependent, and which are ignored.

Constraints: What the agent must never do. Explicit prohibitions prevent catastrophic actions. "Never approve a PR that has failing tests." "Never suggest changes that modify the public API without flagging it as a breaking change."

Output format: Exactly how the agent should structure its responses. JSON schema, markdown template, whatever your downstream systems expect. Ambiguous output formats lead to parsing errors.

Examples: Two to three examples of ideal agent behavior for common scenarios. Examples are worth a thousand words of instruction.

The quality of your system prompt directly determines the quality of your agent. Spend more time on the prompt than on the code. Iterate it based on real outputs. Version control it like you version control code.

Tool Integration

Tools give your agent hands. Without tools, it can only think and talk. With tools, it can act on the world.

Each tool should follow five principles:

Single responsibility. One tool does one thing. "Query database" is a tool. "Query database, format results, and send email" is three tools mashed together.

Typed parameters. Every parameter has a type, a description, and validation rules. The model uses the description to decide what values to pass. If the description is vague, the model guesses. Guesses are wrong.

Clear error reporting. When a tool fails, the error message should tell the agent what went wrong and how to fix it. "Database connection timeout, retry in 5 seconds" is actionable. "Error" is not.

Idempotency where possible. Running the same tool with the same parameters twice should produce the same result. This makes retries safe and debugging easier.

Minimal permissions. The tool should have access to exactly what it needs and nothing more. A database query tool should have read-only access. A file system tool should be scoped to a specific directory. Broad permissions create broad attack surfaces.

Connect tools through MCP for maximum interoperability. Your agent can then use tools from any MCP server, and your tools can be used by any MCP-compatible agent.

Memory Architecture

An agent without memory is like a coworker with amnesia. Every interaction starts from zero. Context that was established five minutes ago is gone. Decisions that were made yesterday are forgotten.

Short-term memory is the conversation context. The current session's messages, tool results, and intermediate reasoning. This is handled by the LLM's context window. The challenge is managing the window size. When context exceeds the window, older information gets truncated. You need a strategy for what to keep and what to summarize.

Long-term memory stores information across sessions. User preferences, past decisions, learned facts, accumulated knowledge. This typically lives in a vector database like Pinecone, Weaviate, or Chroma. The agent queries the vector database with the current context to retrieve relevant past information.

The retrieval quality determines the memory quality. If your embedding model doesn't capture the semantic similarity between the current question and the stored answer, the relevant memory won't be retrieved. Test your retrieval pipeline rigorously.

Episodic memory is a pattern I use for agents that handle recurring tasks. Store a summary of each completed task: what was requested, what was done, what the outcome was, what went wrong. When a similar task arrives, the agent retrieves the episodic memory and benefits from past experience.

Testing Your Agent

You cannot ship an agent without testing it. And agent testing is different from software testing.

Unit tests cover individual tools. Does the database query tool return the expected results? Does the file write tool handle permission errors? These are standard software tests.

Integration tests cover the agent's reasoning. Given a specific input, does the agent select the right tools, in the right order, with the right parameters? These tests are more like scenario simulations than traditional integration tests.

Adversarial tests probe the agent's failure modes. What happens when the user gives contradictory instructions? What happens when a tool returns unexpected output? What happens when the user tries to manipulate the agent into bypassing its constraints?

Regression tests ensure that improvements don't break existing behavior. Every bug you fix becomes a test case. Every edge case you discover becomes a test case. The test suite grows with the agent.

Deployment and Operations

Deploy your agent behind an API. Health checks verify it's responsive. Rate limiting prevents abuse. Authentication ensures only authorized callers can invoke it.

Monitor everything. Token usage per interaction. Latency per tool call. Success rate per task type. Error categories and frequencies.

Set up alerting for anomalies. A sudden increase in token usage might indicate a prompt injection attack or a reasoning loop. A sudden increase in errors might indicate a tool failure or an API outage.

Plan for model updates. When your LLM provider releases a new model version, your agent's behavior might change. Test the new model against your regression suite before switching. Keep the ability to roll back to the previous model version.

Build an agent that you'd trust to work unsupervised. Because that's exactly what it's going to do.