# Producer Brief — Episode 001: Agent Loops

## Topic
Agent Loops — autonomous AI systems that pursue objectives over time through iterative loops of planning, execution, evaluation, and memory.

## Why It Matters
Agent loops are less about making AI smarter and more about changing how intelligence is organized. The pattern is moving from experiments to production: loop engineering is now a named discipline, verification layers are standard practice, and real systems are deployed for analytics, document review, lead qualification, and recurring audits. But there are real failure modes — infinite loops, cost overruns, hallucination reinforcement, goal drift. The conversation about how to build these responsibly is just getting started.

## Seed Question (goes to Leonard first)
"Everyone's talking about agent loops right now — systems that don't just answer a question but actually pursue a goal over time. The question is: are we genuinely entering a new era of autonomous AI systems, or are we just wrapping chatbots in while loops and calling it a revolution?"

## Context Notes (background depth — NOT a script)
- Loop engineering formalized by Addy Osmani (Jun 2026)
- Reflexion pattern: generate → self-critique → revise → repeat until pass or cap
- Separate verifier model/step now standard (don't let model grade its own output)
- Andrew Ng's nested loops model (agentic coding, dev feedback, external feedback)
- Cost control is a common failure point
- Real use cases: Friday analytics summaries, overnight doc error sweeps, lead qual agents, recurring SEO audits
- Risks: infinite loops, hallucination reinforcement, automation bias, security, goal drift, runaway autonomy

## Format
- 8 turns total (Leonard opens, then 4 exchanges each)
- Leonard speaks first
- Max tokens per turn: 250 (opening), 180 (remaining)
- Let the conversation flow naturally after the seed question