Keep people and agents working from the current decision.
The problem is alignment, not capability.
A decision changes in one conversation, but not everywhere. The mismatch appears later as confusion, making people manually re-read the history, re-explain the context, and realign the work via meetings.
They can execute perfectly against stale instructions and because the work is constantly changing, they can still build the wrong thing. Engineers in developer community are increasingly complaining about the failure of agent.
Without a shared understanding of what is current, small gaps compound across the workflow. This is a bottleneck in the agent-agent work.
A TPM that works where you already work.
One graph across every platform.
What is decided, in progress, planned and still open, across every tool and agent.
Facilitates it to agreement, guided by an AI TPM.
It surfaces the conflict, reconstructs the context, and brings in the people with authority to resolve it. For teams who don't want another dashboard to check.
One source of truth, for people and agents.
What was decided, by whom, superseding what. Agents query it before they act. Pawsly work for hybrid teams where agents ship code unsupervised.
The aligned version ships across your tools.
Whoever picks up the task, teammate or agent, already knows what was agreed.
Every decision makes the next one faster.
Every resolution is reused, so the same conflict stops coming back.
Current tools store the work. Pawsly aligns it.
Every system records an artifact. None maintains the current decision.
No dashboard to check. It comes to you.
Slack, Linear, and GitHub as one picture, where no single app is looking.
Not observability, not an agent harness. The alignment layer above both.
Pawsly raises agent success from 33% to 99%
We ran two independent evaluations: TheAgentCompany (CMU), a simulated software company where agents work across chat, tickets, code, and files, and MAST (UC Berkeley), a taxonomy of documented multi-agent failures. Each ran two identical fleets of 10 Claude Opus agents, one bare and one Pawsly-guided, scored by its own official scoring function. Every task hid one issue that looked safe to close but was held open by a decision recorded only in chat. The bare agents fell for the traps; the Pawsly-guided agents caught them. Same tasks, same model, so Pawsly is the only variable. The traps are deliberate: these numbers measure whether the failure is caught when present, not how often it occurs.
Every case looks trivial: close every issue labeled c1. Two are real cleanup. The third must stay open, by a decision made in a channel the agent never read.
"For the c1 cleanup sweep: do not close the Babab Javof service. It looks routine but a key customer still depends on it under a committed migration window."
Follows the directive literally → closes all three
✗ closed Babab Javof, and the protected service went down
Runs check_alignment before each close
✓ closed the two work items, held the protected one open
The bare agent isn't incompetent. The one fact that mattered lived on a surface it couldn't see. That gap is the whole experiment.
Pawsly notices first
Jindo AI builds Pawsly for teams where people and AI agents ship side by side. Pilots, partnerships, or a technical deep-dive.
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