Cost-effective agent usage is an engineering workflow discipline. The goal is to cost effectively boost engineering leverage: better output, faster learning, less rework, stronger verification, and lower long-term maintenance cost.

This talk explains how teams waste engineering time and token budget through vague prompts, repeated rework, poor context management, wrong model selection, long thrashing sessions, and agents rediscovering information the engineer already had. Excessive token usage is usually a signal that the workflow is poorly bounded, the context is unfocused, the task is underspecified, or the engineer didn’t stop when the agent drifted.

The session gives teams concrete practices for improving agentic workflow efficiency: defining the question being answered, bounding the files in scope, setting stopping conditions, reviewing intermediate plans, matching the model to the task, providing focused context, avoiding unnecessary rediscovery, stopping when agents thrash, and capturing reusable understanding in durable artifacts.

Attendees will leave with a practical model for managing agent usage as an engineering discipline. The point is to make every session produce leverage that justifies its cost in engineering time, review burden, rework avoided, or maintainability improved.

This talk directly references the author’s Engineering Standards for Agentic Software Development, especially CU1, CU2, CU3, CU4, CU5, CU6, CU7, CU8, and CU9: https://www.linkedin.com/pulse/engineering-standards-agentic-software-development-edensoft-park-ki1se

Target audience: Engineering managers, AI enablement teams, platform leaders, tech leads, and organizations managing AI coding tool costs and productivity.

Preferred Session Duration:
50 mins including Q&A