An AI-native company scatters decisions across Slack, GitHub and Linear — and now across agents as well as people. When an agent can’t see the decision that governs its task, it ships work that looks finished but is wrong. Pawsly unifies every event into one decision graph so agents can check alignment before they act.
Alignment isn’t an accuracy tweak. It’s what lets an AI-native company take the human reviewer out of the critical path — and finally collect the throughput the agents promised.
An AI-native company scatters decisions across Slack, GitHub, and Linear — and now across agents as well as people. A call gets made in a Slack thread. An agent three surfaces away never sees it, acts on the context it can reach — closes the issue, ships the change — and the result looks finished.
It isn’t. It’s done but wrong: correct against the context the agent had, incorrect against a decision it couldn’t reach. Every such output has to be caught by a human. And once you can’t trust an agent to have seen the relevant decision, you have to review all of its decision-governed work — because you don’t know in advance which piece is wrong. That review step — not model speed, not agent count — is the real productivity ceiling of an AI-native company. Agents solved speed. They did not solve trust.
Pawsly sits beside the agents’ own systems. It collects every event and log the agents produce across those systems into one decision graph, then guides each agent to check that graph before it acts.
A coding agent operates a real company’s stack — GitLab, RocketChat, Plane — to run a task like “close every issue labeled cN.” One look-alike issue is protected by a decision made in chat.
In a multi-agent run each agent works on its own surface, and the failure is the decision never crossing the handoff between them.
Both benchmarks were run and scored with Claude Opus, each using its own official scoring function.
* 33.3% is weight-dependent (bare ranges 25–75% by checkpoint weighting), which is why we anchor on the weight-independent 0→99%. These benchmarks were built to feature the cross-surface-decision failure — the numbers show the fix works when the failure is present, not how often it occurs in any given workload. Compliance today is advisory (~99%, 174/175); enforcement closes the last ~1%.
Pawsly guides a coding agent with the exact context the team has already decided. On the slice it targets, that turns into three things at once:
Productivity — the task actually gets done. Decision-governed work goes from 0% → 99% fully complete: finished, not just attempted, and correct enough to run without a human reviewer gating each action.
Quality — what ships is correct. The agent’s output honors the team’s decisions and acceptance criteria — the right column name, the right key, the full definition of done — instead of looking finished but being wrong.
Efficiency — right context, full automation. Correct context up front means no correction round-trips and no human in the loop, so the coding agent runs end-to-end on its own.
Raw capability. It won’t make an agent write cleverer code, reason more deeply, or catch its own logic bugs. Where no cross-surface decision was missed it’s flat — capability tasks stay at 80–90% with or without it, and the 7 intrinsic MAST modes move 86 → ~95, essentially unchanged. If the agent’s code is wrong for reasons unrelated to missing context, Pawsly won’t rescue it.
That specificity is the point: Pawsly lifts exactly the alignment slice and nothing it can’t. Which is why the productivity and trust are real — the work is correct by alignment, so it runs unsupervised, exactly where an AI-native company’s throughput was leaking.
Without trust, every output routes through a human — which is where the productivity of an AI-native company actually leaks. Pawsly makes agent work correct by alignment on the decisions your team already made, so it can run unsupervised: on the slice it targets, that’s 0 → 99% full completion, MAST 61 → 97, and 188 rescues with zero regressions. It removes the human reviewer from ~99% of decision-governed work today; enforcement closes the rest. That — not raw speed — is the alignment unlock that lets an AI-native company finally get the throughput the agents promised.