Three of the largest model providers have quietly agreed on a shared format for something that, until now, every lab solved differently: what happens when an AI agent needs to pass an unfinished task to another agent — possibly one built by a different company entirely.
The format, referred to informally as a "handoff packet," bundles the state an agent needs to keep working on a task: the original instructions, a compressed history of what's been tried, any tool results still relevant, and a confidence estimate for how likely the receiving agent is to need to backtrack. Previously, this kind of transfer either didn't happen — agents simply failed at the boundary of their own context — or happened through brittle, one-off integrations built for a single pair of systems.
"The failure mode we kept seeing wasn't agents being incapable," said one infrastructure engineer involved in drafting the spec, who works at a mid-sized orchestration startup and asked to speak generally rather than for their employer. "It was agents being capable but starting from zero, because nothing survived the handoff. You'd get an agent that had already ruled out four approaches, hand off to a second system, and watch it try all four again."
Under the new format, a receiving agent can inspect a handoff packet's provenance chain — which systems touched the task, in what order, and what each one changed — before deciding how much to trust the state it inherited. That provenance chain was reportedly the most contentious part of the negotiation: it requires each participating system to expose more about its internal reasoning than some labs were initially comfortable disclosing to competitors, even in summarized form.
The compromise was a tiered disclosure model. A handoff packet always includes the compressed task history, but the confidence estimates and any flagged uncertainty are optional fields that a sending system can choose to omit — at the cost of the receiving agent treating the handoff more conservatively, re-verifying more of the prior work before proceeding.
Early adopters say the practical effect is fewer redundant tool calls and shorter end-to-end task times in multi-agent pipelines, particularly for long-horizon work like multi-step research tasks or codebase migrations that get split across specialized agents. Nobody involved claims the format is elegant. Several people close to the process described it as "the minimum everyone could agree to," which several also framed as the reason it stands a chance of being adopted rather than shelved.
A public draft of the specification is expected within the quarter, alongside reference implementations for the two most common agent runtimes. Whether it becomes a genuine standard, or one more format among several competing ones, will likely depend less on its technical merits than on whether a handful of large customers start requiring it in procurement contracts — which, if the last decade of infrastructure standards is any guide, is usually how these things actually get settled.