Can I switch AI models in the middle of a task?

Yes, if the handoff between models is evidence, not just chat history. When the first model leaves behind verifiable execution receipts, one per completed tool call, the second model can check exactly what ran, what came back, and when, then continue from the verified state instead of trusting the first model's summary.

How do I continue my work when I switch AI models?

Bring both halves of continuity. Export the working state, the goal, the decisions made, what was tried, and the next step, so the new model knows what to do. Then hand over the verifiable receipt chain so it can check what actually ran. The new model reads the state for direction and verifies the receipts for truth, then continues from the checked facts.

Is switching AI models just a memory problem?

Only half of one. Memory moves the plan, but it cannot move trust: a handoff summary is the old model's testimony about its own work, and testimony can be confident and wrong. Verifiable execution receipts supply the missing half, checkable facts about what ran, what returned, and when, which the new model confirms mechanically instead of taking on faith.

Why does switching models usually lose the work?

Because the usual handoff is a transcript: the old model describes what it did, and the new model must trust the description. Transcripts are testimony. The new model cannot distinguish real steps from confident summaries, and any error in the old model's account silently becomes the new model's starting point.

What does the new model need to trust the handoff?

Verifiable execution receipts, minted at execution time by the system that ran the calls. Each receipt carries canonical input and output bytes, a SHA-256 commitment, a timestamp, and a sequence position. The new model recomputes the commitments. What checks out is the verified starting state. Nothing requires trusting the old model.

Does a receipt-based handoff work between different vendors' models?

Yes. The receipt is verified mechanically, not by any model's judgment, so it is model-agnostic. The same property has been demonstrated across independent implementations: an AER-1 receipt chain was checked end to end on an independent implementation against real external values, a worked cross-vendor verification. Cross-model handoffs use the identical mechanism.

What can still go wrong when switching models mid-task?

Receipts prove execution, not judgment. A receipt-based handoff guarantees the new model starts from verified facts about what ran. It does not guarantee the old model's plan was good, its tool choices were right, or its outputs were correct. Verify execution with the receipt, and keep checking correctness the way you always have.

How do I make my agent's work switchable between models?

Run the task through a layer that mints a verifiable receipt per tool call. The handoff packet is then the receipt chain: any model, on any vendor's API, can verify it and continue. The live demo mints a real receipt you can inspect, free, no signup.

Switch AI Models Mid-Task Without Losing Proof: Verifiable Handoffs

Why model switches usually lose the work

The usual handoff between models is a transcript: the old model describes what it did, and the new model picks up from that description. That makes the old model the only witness to its own work, and transcripts are testimony. The new model cannot tell a real step from a confident summary, cannot spot the call that was silently skipped, and cannot tell whether the results it is building on actually came back from the tools.

It gets worse. Studies this month confirmed that agents can delete their own execution traces, so the transcript is not just unverifiable, it is editable by the party being verified. Any error in the old model's account silently becomes the new model's starting point, and it compounds from there. Switching models on testimony is starting over with extra steps.

Continuity has two halves: memory and proof

When people ask how to continue work when switching AI models, they usually mean memory: keep the context, the decisions, the plan, the next step. Memory is the first half of continuity, and it is the half most handoff advice already covers. Export the working state, hand the new model a clean summary, and it knows what you were doing.

But memory alone is testimony. The new model inherits what the old model said it did, and a confident summary can be wrong in ways no summary can detect. The second half of continuity is proof: verifiable execution receipts, minted at execution time by the system that ran the calls, that let the new model check what actually ran instead of trusting the handoff note. Memory tells the new model what to do next. Proof tells it what is true.

When both halves travel, switching models stops being a migration and becomes a non-event. The two-halves framing is worked through in detail in The AI handoff problem: why switching models wipes your work (and what actually fixes it), which pairs portable state with portable proof and shows why neither half is enough alone.

What a receipt-based handoff looks like

Run the first half normally. Every tool call mints a verifiable execution receipt at execution time: canonical input and output bytes, a SHA-256 commitment, a timestamp, and a sequence position.
Hand over the receipt chain. The handoff packet is the ordered receipts, not the chat. A missing call shows up as a visible gap in the sequence, so nothing can vanish silently.
The new model verifies first. It recomputes each commitment from the canonical bytes. Anything that fails is re-run or discarded, not inherited.
Continue from the verified state. The new model proceeds from the recomputed facts, and its own calls mint receipts in the same chain. The switch point is itself checkable.

Nothing in this loop requires trusting either model. The receipts are verified mechanically, which is what makes the handoff model-agnostic: any model can check them, regardless of vendor.

Worked example: verified across vendors, not just models

The same mechanism has already been demonstrated across fully independent implementations. The independent AER-1 interop run checked an AER-1 receipt chain end to end on an independent implementation against real external values, and the full chain was double-verified. Cross-model handoffs use the identical mechanism: one side mints the evidence, any other side checks it, with no shared platform and no mutual trust.

The format is also being adopted for execution-evidence runtimes beyond chat agents. A FlyThink interop proposal, covering execution evidence for physical systems, was accepted this week and its test fixture is still pending. Independent implementations are tracked in the public AER-1 registry.

The honest boundary

A receipt-based handoff proves the new model starts from verified facts about what ran. It does not prove the old model's plan was good, its tool choices were right, or its outputs were correct. Receipts prove execution, not judgment. A model can faithfully execute a bad plan and mint perfect receipts for it, and the new model inherits those receipts, not wisdom.

Verify execution with the receipt, and keep checking correctness the way you always have. The handoff guarantees continuity of evidence. Judgment about the plan is still yours.

Check one yourself

The live demo runs one real tool call and mints a real verifiable receipt, free, no signup. That receipt is the same object a handoff packet is built from. Inspect it, recompute the commitment, and you will have run the verification step any new model performs before continuing.

Mint a receipt and run the handoff check right now.

For the full walkthrough, see how to switch AI models mid-task without losing context.