How do I start making my first AI agent?

Pick one narrow task with a measurable outcome, then assemble three things: a model, the tools it can call, and instructions describing its job and its limits. Wrap them in a loop that runs until an exit condition fires. The first step is writing down what success means and what the agent must never do, before you choose a framework or a model.

What do you need to build an AI agent?

A model to reason, tools it can call to read data and take action, instructions that define the job and its boundaries, a loop with explicit exit conditions, and somewhere durable for state to live. The last one gets skipped most often, and it is what determines whether the agent can resume after a failure.

How long does it take to build an AI agent?

A working prototype takes hours to days for someone comfortable with an LLM API. Getting the same agent ready to run unattended takes substantially longer, because durable state, scoped credentials, audit logging, and idempotency are each real engineering work. Any specific timeline depends on how many systems of record the agent writes to.

Where does an AI agent store its memory between runs?

In whatever durable store you give it, usually a database. The context window holds the current run and disappears when the run ends, which means an agent relying on it alone cannot tell what it already processed. After a restart it will redo completed work and reapply side effects. Recording each action against a stable key in a database prevents that and allows resumption.

How do you keep an AI agent secure in production?

Give each tool its own credential scoped to exactly what that tool does, rather than one key with broad write access. OpenAI's guide recommends rating each tool by read versus write access, reversibility, and financial impact; use that rating to set both the credential scope and whether a human approves the call. Log every action so it stays attributable afterwards.

How to Build AI Agents That Survive Run 1,000

Every guide on the first page teaches you to get one run working. None of them covers what breaks on run 1,000: state that vanishes, token spend that climbs, credentials with no scope, and no record of what the agent did. Here is the build, through to production.

Rahul RamakrishnanSeptember 16, 2026

Schematic of an AI agent loop at run one and at run one thousand, showing durable state, scoped credentials, and an audit record.

Key takeaways

An AI agent is a model given tools, instructions, and a loop that runs until an exit condition is met.

Frequently asked questions

How do I start making my first AI agent?: Pick one narrow task with a measurable outcome, then assemble three things: a model, the tools it can call, and instructions describing its job and its limits. Wrap them in a loop that runs until an exit condition fires. The first step is writing down what success means and what the agent must never do, before you choose a framework or a model.
What do you need to build an AI agent?: A model to reason, tools it can call to read data and take action, instructions that define the job and its boundaries, a loop with explicit exit conditions, and somewhere durable for state to live. The last one gets skipped most often, and it is what determines whether the agent can resume after a failure.
How long does it take to build an AI agent?: A working prototype takes hours to days for someone comfortable with an LLM API. Getting the same agent ready to run unattended takes substantially longer, because durable state, scoped credentials, audit logging, and idempotency are each real engineering work. Any specific timeline depends on how many systems of record the agent writes to.
Where does an AI agent store its memory between runs?: In whatever durable store you give it, usually a database. The context window holds the current run and disappears when the run ends, which means an agent relying on it alone cannot tell what it already processed. After a restart it will redo completed work and reapply side effects. Recording each action against a stable key in a database prevents that and allows resumption.
How do you keep an AI agent secure in production?: Give each tool its own credential scoped to exactly what that tool does, rather than one key with broad write access. OpenAI's guide recommends rating each tool by read versus write access, reversibility, and financial impact; use that rating to set both the credential scope and whether a human approves the call. Log every action so it stays attributable afterwards.