SpecShift Labs helps AI teams review one concrete agent workflow before it is rolled out more broadly.
The pilot is built for teams asking practical questions:
Did the agent use the right evidence?
Did it stay inside its scope?
Did it hand off correctly?
Did it know when to escalate?
Did it claim completion before the work was actually complete?
Did it have a recovery path when something went wrong?
SpecShift reviews the observable workflow record and selected bounded synthetic review scenarios, then returns a structured candidate-discrepancy memo for human review.
The goal is not to certify the system. The goal is to help engineering, product, and risk teams see where an agentic workflow may be fragile before those issues become expensive integration, customer, or operational problems.
A focused diagnostic pilot may examine whether the workflow shows signs of:
low-quality or unverified inputs being treated as reliable evidence
stale data being treated as current
unclear agent scope or approval boundaries
false task-completion claims
weak handoffs between agents, systems, or humans
missing escalation or recovery paths
claims that are stronger than the evidence supports
In plain English:
Before your AI agent goes wider, SpecShift helps check where the workflow may claim too much, trust the wrong evidence, miss a handoff, or fail without a clear recovery path.
A pilot may include:
one scoped workflow review
selected bounded synthetic review scenarios
a candidate-discrepancy memo
an evidence, handoff, scope, and recovery-path review
a limitation and boundary map
recommended design-review next steps
The output is designed for engineering, product, and risk teams. It gives your team a structured way to decide what needs attention before broader rollout.
View Sample Candidate-Discrepancy Memo
SpecShift does not certify AI systems.
This is not a benchmark, compliance audit, runtime monitor, security tool, deployment approval, or guarantee of AI safety.
SpecShift provides architecture-stage diagnostic review for human decision-makers. Your team keeps control of deployment, interpretation, and next steps.
Choose one workflow
Start with one AI-agent workflow where trust, evidence, handoffs, or completion claims matter.
Define the review scope
We identify the workflow boundary, claimed behavior, relevant risks, and what materials can be reviewed.
Run bounded stress scenarios
SpecShift uses selected synthetic scenarios to examine how the workflow handles evidence, scope, escalation, handoffs, and recovery.
Return a candidate-discrepancy memo
The memo identifies where the workflow may require design attention before wider use.
Human review remains in control
SpecShift does not approve deployment. Your team uses the memo to decide what to fix, test, narrow, or escalate.
Logs show what happened.
Evals test performance.
Monitoring watches runtime behavior.
SpecShift reviews whether claims, evidence, and workflow behavior line up before broader rollout.
SpecShift also maintains a public-safe prototype evaluator for generated-code tasks.
The demo illustrates a simple point:
Passing visible tests is not the same as satisfying the full specification.
The public prototype is separate from the protected SpecShift review method.
View Public Prototype on GitHub
The best place to begin is one high-value workflow and one bounded review.
Tell us:
what workflow you are using
what the agent claims to do
what risk concerns your team
what decision a review would help you make
Please do not send confidential data, source code, model internals, private prompts, customer records, or sensitive logs in the first message. If there is a fit, SpecShift will establish the right confidentiality, scope, and review boundaries before looking at anything sensitive.
Start with one workflow. SpecShift checks where the claims, evidence, and behavior may not line up. If your team is preparing to deploy an AI workflow, start with one bounded review and one concrete question. We'll help your team review whether the workflow's claims, evidence, and behavior appear aligned within the agreed review scope before broader rollout.