Bullseye captures the why behind your work — including what you tried and rejected — and serves it as living context to Claude Code, Cursor, and Copilot. So your agents build from current org intent, not a stale ticket.
Point an agent at a task and it moves fast — toward whatever intent it can piece together. But the why behind the work isn't written down in one place. It lives across product docs, design files, tickets, chat, and code — maintained by different people, none of whom wrote it for a machine to read. The agent inherits a goal with the reasoning stripped out, fills the gap with a guess, and ships it at lightning speed. And whoever is driving that agent — an engineer, or increasingly a non-engineer building with LLMs — wasn't the one who set the why either. The result is rework: output that has to be walked back to match what the org already decided.
The why has many authors and no owner — product sets the goal, design bounds it, engineering decides the how, leadership shifts the priority. Four tools, none of them reconciled.
A ticket is one person's slice of intent, frozen at one moment. The agent inherits that sliver — blind to every decision made around it since.
"We tried that, it broke prod" lives in a chat thread, not the code. The most decisive context leaves zero trace where the agent reads.
A hand-written CLAUDE.md is one engineer's snapshot of a why a dozen people keep changing. It's stale the moment someone else moves.
Bullseye harvests durable intent — decisions, constraints, and the alternatives you rejected — from the artifacts your org already produces: code, tickets, coding sessions, docs, and chat. No wiki. No forms. Capture is a byproduct of how you already work.
It resolves intent across sources, retires what's stale, and surfaces genuine disagreement instead of guessing. The right source wins for the right kind of claim.
Confirmed intent lands in your agent's context — each goal beside the constraints that bound it — so it derives the right how, not just the what.
Intent isn't in one place. The why behind your product lives in docs, chat, and meetings; the how lives in your code and coding sessions. Bullseye reads the artifacts your org already produces — across both — and when they disagree (the doc says one thing, the code does another), it surfaces the gap instead of guessing.
Every knowledge base dies because writing it down is a chore nobody owns. Here the intent is harvested from the work itself — and the agent that benefits is the same one that produced it. You document for yourself, tomorrow, for free.
"We tried that, it broke prod" is the most valuable thing in your org and the first thing lost — it never survives into the final code. Bullseye keeps the rejected paths and the reasons, so your agent doesn't re-walk them.
Intent without limits makes an agent build the impossible. Bullseye pairs each goal with the feasibility that bounds it, so the agent reconciles them in-context instead of guessing.
When your org hasn't actually settled a decision, Bullseye withholds the options so the agent can't pick a side — it keeps building on reversible moves while the call routes to whoever owns it. Settled, low-stakes facts confirm themselves; humans decide only when it matters, never in the keystroke loop.
The model writes the code. Your org owns the why.
Frontier models get better for everyone equally. Bullseye makes your agent sharper about your org — a private, compounding advantage built from a decision history no model lab can see. The more your org works, the more it knows.
Across governed decision scenarios where an org's intent diverged from generic best practice, agents given Bullseye's served intent followed the org's actual decisions far more often than agents without it — adherence rose from ~40% to ~93% (measured at n=100 per scenario, a blind cross-vendor judge), a +53-point lift. The gains were largest on the non-obvious rules a fresh agent gets wrong — on one destructive-migration rule, an unaided agent took the unsafe path in 100 of 100 trials; served the decision, it was safe 99 of 100. Because measuring the gap between intent and output is built in, you can prove the context is working — not take it on faith.
And it isn't just "more context." In a controlled test, we served agents stale intent — a decision the org had already superseded. They did worse than agents given nothing at all and overwhelmingly followed the outdated directive — a result that replicated across two model setups. What moves the needle is intent that's current — which is exactly what Bullseye keeps.
Read the full internal evaluation: scenario design, scoring method, stale-intent negative control, limits, and what the findings mean for orgs adopting AI coding agents.