METHOD & PROOF
How DVC turns agent work into a reliability verdict.
This is no longer a product catalog. It is the operating method behind the AI Agent Reliability Diagnostic: trace the workflow, stress the context, name the failure modes, and decide whether to build.
OPERATING METHOD
PUBLIC OFFER
Front door
Reliability Diagnostic
A fixed two-week review of one agent workflow, with a written reliability verdict, failure-mode map, and remediation plan.
Deep dive →
INTERNAL METHOD
Adversarial read
Council Review
Builder, reviewer, and QA lanes inspect the workflow from different angles so one confident model family does not become the whole truth.
Deep dive →
INTERNAL METHOD
Memory and tools
Context Trace
Retrieval paths, source authority, tool permissions, handoffs, and write-back loops are traced as one operating surface with a living Vault context map.
Deep dive →
NEXT GATE
Implementation decision
Build Scope
Only after the verdict does DVC define whether to build, what to fix first, and where the acceptance gates belong.
Deep dive →
WHAT WE TEST
Intent Drift
Where the agent's output remains plausible while the workflow slowly stops matching what the business actually wanted.
Memory Staleness
Where retrieval pulls the wrong source, misses the newest decision, or treats low-authority context as operational fact.
Tool Overreach
Where an agent can call, mutate, notify, spend, publish, or delete without a clearly written authority boundary.
Human Handoff Gaps
Where escalation exists in someone's head but not in the workflow, leaving edge cases to become silent failures.
Audit Blind Spots
Where the system can act but cannot explain who decided, what evidence was used, or which acceptance gate passed.
Build Readiness
Whether the workflow deserves a bigger implementation, needs a narrow guardrail pass, or should be killed for now.
START WITH THE DIAGNOSTIC
Bring one risky workflow. Leave with a decision.
The internal stack matters because it produces a sharper external artifact: a reliability report your team can act on.