Building AI Agents: From One LLM Call to a Reasoning Loop

Description and use

<h2> Contents </h2> <ul> <li>One call answers. An agent finishes the job.</li> <li>What an agent actually is</li> <li>The agent loop, drawn out</li> <li>One call vs. chain vs. agent, pick the smallest thing that works</li> <li>A minimal agent loop you can read</li> <li>Planning, memory, and context</li> <li>Guardrails: tools are the attack surface</li> <li>Failure modes that bite in production</li> <li>Takeaways</li> <li>Where to go next</li> </ul> <h2> One call answers. An agent finishes the job. </h2> <p>You wire up an LLM, send a prompt, get a paragraph back. It's impressive, until the task

Problem

<h2> Contents </h2> <ul> <li>One call answers. An agent finishes the job.</li> <li>What an agent actually is</li> <li>The agent loop, drawn out</li> <li>One call vs. chain vs. agent, pick the smallest thing that works</li> <li>A minimal agent loop you can read</li> <li>Planning, memory, and context</li> <li>Guardrails: tools are the attack surface</li> <li>Failure modes that bite in production</li> <li>Takeaways</li> <li>Where to go next</li> </ul> <h2> One call answers. An agent finishes the job. </h2> <p>You wire up an LLM, send a prompt, get a paragraph back. It's impressive, until the task

Outcome

<h2> Contents </h2> <ul> <li>One call answers. An agent finishes the job.</li> <li>What an agent actually is</li> <li>The agent loop, drawn out</li> <li>One call vs. chain vs. agent, pick the smallest thing that works</li> <li>A minimal agent loop you can read</li> <li>Planning, memory, and context</li> <li>Guardrails: tools are the attack surface</li> <li>Failure modes that bite in production</li> <li>Takeaways</li> <li>Where to go next</li> </ul> <h2> One call answers. An agent finishes the job. </h2> <p>You wire up an LLM, send a prompt, get a paragraph back. It's impressive, until the task

Evidence

Building AI Agents: From One LLM Call to a Reasoning Loop

Source