Staff Software Engineer

Coding agents are becoming more capable and able to take on more complex engineering tasks. The next step is to delegate more work to the agent you already use, letting him run autonomously with less supervision.
This workshop gives you a continuous improvement loop. Delegate a bounded task, inspect where the agent needs help, and improve its harness: the context, rules and skills around it.
Then use evals to test whether those changes actually help. Build a small set of representative tasks and measure task success, reliability, and the human intervention required.
This new model of work will create a substantial increase in output which will expose new bottlenecks. Planning, missing context, coordination, review, and verification will become the work that demands most of your attention.
To improve delivery, we'll also look across the SDLC: where does work get stuck, what can your agent help with, and where does human judgment matter most?
You'll leave with a practical method for increasing your agent's autonomy over time, so you can delegate more useful work and keep improving how it moves from idea to production.
Delegate more work, improve your agent's autonomy with harness engineering and evals, and address bottlenecks across the SDLC.
Define bounded tasks with clear outcomes.
Decide what your agent can execute autonomously and where human judgment remains essential.
Coordinate independent agent tasks.
Give your existing agent the context, rules and skills it needs.
Turn engineering standards into executable checks using types, linting, tests, and CI.
Require proof of work so the agent can verify its results and make its output easier to validate.
Inspect traces and failed runs to identify where your agent is failing.
Build a small eval set from representative tasks and recurring failures.
Compare harness changes using metrics like task success, reliability, success rate and cost.
Identify where work gets stuck in planning, coordination, review, testing, and release.
Assess why more code does not always translate into faster software delivery.
Prioritize constraints that limit completed work or demand repeated human attention.
Understand why faster code generation does not automatically lead to faster delivery. Learn how bottlenecks shift across the SDLC and how to optimize for flow, not just coding speed.
Learn how to divide work between humans and agents, structure tasks for delegation, coordinate parallel cloud agents, and optimize for human attention while maintaining quality.
Explore common bottlenecks from planning to production. Learn where agents can safely help, where human judgment remains essential and prioritize the workflows where autonomy creates the most value.
Build the environment agents need to work reliably with: context, tools, permissions, executable constraints, proof of work and verification
Use traces, failed runs, and evals to understand where workflows break down. Improve rules, context, skills, tools, and subagents, and increase autonomy based on evidence.
Software engineers using AI coding tools looking to improve their team SDLC
Tech leads and engineering managers who want to understand how to increase engineering output using AI
Engineering leaders who adopted AI tools but aren't seeing it in delivery speed or business outcomes and want to understand why and fix it

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1–3:30pm EDT