AI agents can write code, investigate repositories, run tools, and tackle increasingly complex engineering tasks. But every extra loop, unnecessary tool call, bad assumption, and failed attempt consumes more AI credits. The biggest opportunity is not simply using fewer tokens. It is getting higher-quality work from the credits you already spend.

In this practical, developer-focused session, we’ll look inside the agent loop to understand how models, context windows, tools, and multi-step workflows affect both quality and consumption. You’ll learn how to choose the right model for the job, provide focused context, avoid context rot, and structure complex work as research → plan → implement instead of throwing one enormous task at an agent.

Then we’ll move beyond prompting. We’ll explore how tests and other deterministic guardrails stop mistakes from compounding, and how persistent instructions, custom agents, skills, MCP, prompt files, and subagents can make important workflows more repeatable and efficient. We’ll finish with power-user techniques such as choosing CLI tools strategically, reducing oversized tool output, collapsing tool calls, and using analytics such as /chronicle to learn where your agent workflows can improve.

The goal is simple: spend AI credits on useful engineering work, not rework. You’ll leave with practical techniques for making agents more accurate, predictable, and valuable without turning token counting into your new full-time job.

Takeaways
- Choose the right model and context for each task.
- Structure agent workflows to reduce rework and wasted credits.
- Use guardrails and reusable agent assets to improve quality at scale.