Hi — I’m Oluseyi Christopher Olusola, founder of Consistency, a runtime recovery system for autonomous AI workflows.
I’ve been looking at AutoResearch’s existing recovery model, particularly its stateful execution, persisted experiment artifacts, Forge checkpoints, completed-seed skipping, and pending-seed resumption.
I’m interested in testing a complementary recovery problem.
AutoResearch can already preserve completed work and resume interrupted execution. But in a research workflow, work that completed successfully can later turn out to be semantically untrustworthy — for example, if a source, intermediate assumption, experiment result, or review input is later found to be invalid after downstream stages have already consumed it.
I’d like to run a small controlled pilot comparing:
AutoResearch’s existing checkpoint/resumption path, and
validity-aware selective recovery using Consistency.
In the second case, Consistency would attempt to identify the smallest affected recovery boundary, preserve independent valid work, invalidate only dependent state, repair the affected stage, and selectively continue.
We would measure:
final correctness / evidence integrity;
repeated model and tool work;
stages or work preserved;
elapsed recovery time; and
whether downstream state affected by the injected invalidity is correctly rebuilt.
This is not intended to replace AutoResearch’s checkpointing, persistence, or resume mechanisms. The distinction I want to test is:
Persistence tells us what completed. Validity-aware recovery asks what completed work is still safe to reuse.
Consistency is proprietary, so the pilot would use its external SDK/API surface without requiring access to its internal recovery logic.
In our existing controlled 12-trial tool-failure benchmark, Consistency recovered successfully in 12/12 trials while using 33.3% fewer model calls and 66.7% fewer tool calls than full workflow restart. Those results are specific to that controlled scenario; the purpose of this proposed pilot is to test the idea independently in a different agent workflow.
https://consistency-runtime.netlify.app/
If this sounds useful, I can share a concise pilot protocol and a proposed controlled failure scenario first.
— Oluseyi Christopher Olusola
Founder, Consistency
Hi — I’m Oluseyi Christopher Olusola, founder of Consistency, a runtime recovery system for autonomous AI workflows.
I’ve been looking at AutoResearch’s existing recovery model, particularly its stateful execution, persisted experiment artifacts, Forge checkpoints, completed-seed skipping, and pending-seed resumption.
I’m interested in testing a complementary recovery problem.
AutoResearch can already preserve completed work and resume interrupted execution. But in a research workflow, work that completed successfully can later turn out to be semantically untrustworthy — for example, if a source, intermediate assumption, experiment result, or review input is later found to be invalid after downstream stages have already consumed it.
I’d like to run a small controlled pilot comparing:
AutoResearch’s existing checkpoint/resumption path, and
validity-aware selective recovery using Consistency.
In the second case, Consistency would attempt to identify the smallest affected recovery boundary, preserve independent valid work, invalidate only dependent state, repair the affected stage, and selectively continue.
We would measure:
final correctness / evidence integrity;
repeated model and tool work;
stages or work preserved;
elapsed recovery time; and
whether downstream state affected by the injected invalidity is correctly rebuilt.
This is not intended to replace AutoResearch’s checkpointing, persistence, or resume mechanisms. The distinction I want to test is:
Persistence tells us what completed. Validity-aware recovery asks what completed work is still safe to reuse.
Consistency is proprietary, so the pilot would use its external SDK/API surface without requiring access to its internal recovery logic.
In our existing controlled 12-trial tool-failure benchmark, Consistency recovered successfully in 12/12 trials while using 33.3% fewer model calls and 66.7% fewer tool calls than full workflow restart. Those results are specific to that controlled scenario; the purpose of this proposed pilot is to test the idea independently in a different agent workflow.
https://consistency-runtime.netlify.app/
If this sounds useful, I can share a concise pilot protocol and a proposed controlled failure scenario first.
— Oluseyi Christopher Olusola
Founder, Consistency