Make engineering faster without breaking trust.
More code is easy now. Trusted software is still hard. We work with engineering leaders and staff engineers to make the repo, CI, review loops, and product/design handoffs ready for AI-assisted work.
Repo, product, and design systems checked before we scale usage.
Agent work is scoped, verified, routed, and reviewable.
Forward-deployed operator-engineers can work alongside your team.
$ assign ENG-1427 --agent background [context] loaded repo rules, component map, error budget [scope] migrate billing table → new design system [draft] opened PR #1842 with 6 files changed [verify] unit ✓ visual ✓ a11y ✓ bundle ✓ [review] routed UI diff to design, data path to staff eng next: human review in the right place, not everywhere
Agent-ready codebases are designed.
If a strong new hire cannot get productive in your repo, agents cannot either. We start with the system around the work.
Every agent starts cold. Architecture decisions live in Slack, old PRs, or one engineer's head.
Agent rules, repo maps, examples, ownership notes, and the new-hire test.
Slow tests, flaky CI, unclear local setup, review cycles that were already too expensive.
Fast verification, trustworthy CI, narrow checks, legible failure modes.
Humans review too much low-signal output and stop trusting the system.
Scoped agent work, labels, routing, quality gates, and reviewer contracts.
PMs and designers can prototype faster than engineering can absorb.
Prototype-to-production paths with standards, review, and ownership.
Want to know where engineering is actually blocked?
We can start with a focused readiness pass across repo context, CI, review, and product/design handoff, then turn the highest-leverage gaps into a practical plan.
AI acceleration only compounds when the team can see what good looks like.
Faster code only helps if the team knows what to point it at, how to pass the right context, when a human needs to step in, and whose workflows are worth copying.
Keep agent work always on, but never unowned.
Background agents should always be picking up work. The operating system is the router: context quality, blast radius, ownership, and reviewer capacity decide what runs now and what waits.
Logs, owner, failing test attached
Scope needs route, states, data contract
High review drag, low blast radius
Shape work AI can actually carry.
PMs learn how to write specs, experiments, and prototypes that give agents enough context without handing engineering a mess.
## Goal Reduce checkout retry failures for high-value carts. ## Constraints - preserve fraud checks - no schema migration - behind flag: retry_flow_v2 ## Acceptance - retries succeed or fail with reason - visual diff approved - metric: recovery rate +12%
Prototype that make it past the presentation into production.
The 2026 handoff is often a working prototype. Agents still need the production contract: routes, data, states, tests, ownership, and a review path that keeps quality intact.
Live preview, generated app, or product spike with the behavior worth keeping
Route, data shape, states, auth, flags, accessibility, and test expectations
Owner, visual diff, acceptance checks, and the human reviewer who can say yes
Make AI use visible, social, and high-status.
Your strongest builders become the pattern. We package their workflows, spotlight the results, and make the rest of the team want to catch up.
checkout recovery agent + PR review routing
CI flake triage, repros, and fix-forward PRs
prototype-to-PR hardening for onboarding
agent playbooks for migrations and test coverage
We turn your people and pipeline into a product.
Engineering acceleration is not “pick an agent and let it open PRs.” It is redesigning how work moves: which harness runs, how context is loaded, CI/CD that does it's job fast, and how the team chooses the next thing worth shipping.
Talk to us about AI engineering acceleration.
Tell us where engineering is already using AI, where trust breaks down, and what your codebase makes hard.