Efficiency

AI is one of the largest new line items in the engineering budget — model access, tokens, the compute agents trigger, and the people who supervise them. The return on it is not how much the agents generate; it is how much of that spend ends up as software that ships. Today a large share doesn't: agents loop on flaky failures, abandon tasks they can't diagnose, collide with each other on merge, or ship regressions that get reverted — all of it billed, none of it landed. Loop quality is a key lever on how much of that investment converts into merged work. Context Engineering and its MCP Servers give each agent the build and failure context to fix what broke instead of guessing at it across repeated runs; Build Failure Agent lets it self-correct rather than abandon; and Flaky Tests Detection keeps it from chasing — or shipping around — failures that were never real. The merge collisions are a feedback-speed problem: Universal Cache, Predictive Test Selection, and Test Distribution shorten the loop so each change merges before parallel agents collide with it — and the slower the loop, the more collisions you pay to untangle. Artifact Governance tracks which agent-initiated changes reached production — the realization rate — and how much of your AI spend never ships. The full case for the AI program's return lives on AI.

CI compute leaks three ways at once: work you've already done gets redone, builds that fail on a flaky test or a brittle toolchain get rerun, and the machines you pay for sit half-idle. Universal Cache reuses build outputs, dependencies, and toolchains so settled work isn't recomputed, and Predictive Test Selection runs only the tests a change touches. Flaky Tests Detection and Develocity Agents cut the flaky-test and toolchain failures that force reruns, so you don't pay twice to verify a change that was fine all along. And Performance Insights goes after a different kind of waste entirely — idle cores, disabled parallelization, an over-provisioned machine — with agents acting on it, not just charting it. Every lever reclaims the same budget: more delivery from the CI spend you already carry, not a bigger bill.

Every token an agent spends is a hard dollar, whether it lands a merge-ready change or just churns — and as you run more agents, that bill rises right along with the output. A significant share of that spend is waste: an agent reasoning over raw logs ingests tens of thousands of low-density lines, chases false paths, and re-runs builds to reproduce what the logs never captured. Build Scan gives the agent a wide, queryable record to read the cause from, Context Engineering and MCP Servers bound what enters its context, Build Failure Agent hands it the cause instead of a log to re-derive, and Flaky Tests Detection stops it debugging failures that were never real. The cost per shipped change falls and holds as you add agents, so output can grow faster than the bill.

Developer time is the most expensive capacity your delivery has, and much of it never reaches product work, draining into waiting on builds and tests, chasing failures the change never caused, and diagnosing what broke. Universal Cache and Predictive Test Selection cut the wait between change and trusted result, and Test Distribution fans the rest across a pool of agents, so engineers stay in flow. Flaky Tests Detection quarantines non-deterministic tests so they stop failing builds that were fine; when a build does break, Build Failure Agent takes a developer from it failed to the root cause over the build record, not a manual hunt across logs. Develocity Agents keep that loop fast as the codebase grows. The reclaimed hours come back as engineering capacity — including faster, more reliable delivery; see DORA.