Pilot validation — diagnostic workflow under controlled rollout
SDVM turns workflow traces into structured PRE/POST/DELTA diagnostic reports, helping teams identify where degradation concentrates, how strong the evidence is, and what edge-focused tuning step should be tested next.
SDVM refers to four diagnostic dimensions: Synchrony, Depth, Vulnerability and Metacognition.
SDVM pilots start from one recurring workflow, comparable runs, available traces or logs, and a technical owner able to test a narrow intervention.
In long-horizon agentic workflows, the most expensive failures are often the ones that look like normal operation. Steps get skipped without triggering errors, repairs accumulate across cycles, handoffs introduce noise, and plausible outputs can mask gradual workflow drift.
The visible failure often appears late, after the workflow has already drifted.
Stable execution
Skipped steps, repair pressure and handoff noise accumulate while the run still looks plausible.
The failure signal appears late
SDVM works as a controlled diagnostic cycle.
A typical SDVM output is not another trace view. It is a compact diagnostic view of how a workflow changed, what evidence supports the assessment, and what tuning decision should be tested next.
Synthetic example — not client data PRE/POST/DELTA excerpt
| Signal | PRE | POST | DELTA |
|---|---|---|---|
| Repair pressure | 3.1 repairs / cycle | 1.2 repairs / cycle | −61 % |
| Handoff noise | 4 of 7 handoffs flagged | 1 of 7 handoffs flagged | −75 % |
| Step skip rate | 4 skips observed | 1 skip observed | −75 % |
| Evidence strength | 0.51 | 0.74 | +0.23 |
| Interpretation limit | Medium | Low-medium | Stronger, not definitive |
Decision: retune the checkpoint/handoff edge before expanding workflow scope.
SDVM recommendations are designed to focus tuning on the workflow edges where the diagnostic signals concentrate, rather than applying generic fixes to the entire workflow.
Example only. Values are illustrative; actual reports depend on trace quality, workflow structure and available evidence.
SDVM has evolved from a diagnostic concept into a structured pilot workflow for trace-based analysis, report generation and controlled tuning review.
Observability answers: what happened? SDVM helps answer: how is the workflow degrading across cycles? In many cases, SDVM can start from traces your existing tools already capture, then identify whether additional capture is needed for a meaningful PRE/POST comparison. It adds a diagnostic interpretation layer: where the workflow is drifting, how strong the evidence is, and what targeted tuning step should be tested next.
SDVM is a diagnostic and tuning layer for agentic workflows. It sits on top of existing observability surfaces and converts trace evidence into structured workflow diagnosis.
SDVM does not replace tracing, monitoring or evaluation tools. It organizes drift patterns, repair accumulation, handoff friction and step deviation as a structured diagnostic report rather than a dashboard event stream.
SDVM pilots are intentionally narrow: evaluate whether structured trace diagnosis can identify degradation patterns and guide one controlled workflow intervention. Preferred early pilots are coding or bugfix-style workflows, but the fit is structural — recurring task types, traceable multi-step execution, observable repairs or handoffs, and enough comparable runs for PRE/POST analysis.
A strong SDVM pilot candidate usually has one recurring workflow, comparable runs, trace or log evidence, observable repairs or handoffs, and one technical owner able to test a narrow intervention.
Typical pilot shape: 2–4 weeks, depending on trace availability and team cadence.
SDVM is designed to be workflow-engine agnostic. The first validation track remains narrow by design: traceable, recurring agentic workflows with enough comparable runs for PRE/POST/DELTA analysis. Coding and bugfix-style workflows remain a preferred early track because they provide repeatable, multi-step runs with clear intervention points.
The current validation path remains Langfuse-first, while the broader design is intended to remain surface-agnostic. Future compatibility paths may include observability surfaces such as Phoenix/OpenInference and LangSmith, as well as workflow artifact surfaces such as issues, pull requests or commits when relevant to the pilot.
The goal is to validate diagnostic usefulness, report quality, evidence thresholds and tuning guidance under real or semi-real workflow conditions.
Pilot analysis can be performed on anonymized traces and metadata. Data scope, access method and retention expectations are agreed case by case. NDA support is available when required.
Review the pilot intake template · See a readiness probe example
If your workflow looks partially ready, email a short summary and we can scope a controlled pilot conversation.
SDVM is being developed by Ibrahim José Jamhour, an independent researcher working on Distributed Relational Cognition and the operational risks of agentic systems. The work builds on published research, a formal SDVM V3 technical specification and ongoing validation on AI-assisted workflows.
Jamhour also brings prior executive experience in institutional finance and risk-sensitive operations, including the Stanford Sloan Fellows program.