Assisted pilot — trace-first coupling diagnostics
SDVM turns workflow traces into structured PRE/POST/DELTA diagnostic reports, helping teams identify where structural friction concentrates, how strong the evidence is, and what edge-focused tuning step should be tested next—paired with outcome KPIs, not as a replacement for them.
SDVM begins as a trace-first diagnostic layer for coupling health and map gaps, then scales toward Artifact X / Tier ≥2 instrumentation for stronger semantic drift analysis.
SDVM pilots start with workflow mapping, observability scoping, and trace readiness—then a trace-first diagnostic assessment on one recurring workflow with comparable runs 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. Ordinary output-only evaluation may miss these mid-trajectory coupling problems.
The visible failure often appears late, after the workflow has already drifted. This is the problem space SDVM targets. Trace-first diagnostics can surface structural signals today; semantic silent-drift analysis requires richer observability.
Stable execution
Skipped steps, repair pressure and handoff noise accumulate while the run still looks plausible.
The failure signal appears late
Agentic workflows are moving from experiments into operational settings, but reliability, governance, and failure diagnosis remain unresolved. Recent research and enterprise analysis describe overlapping gaps in how agentic systems behave, degrade, and fail across multi-step workflows — the same operational questions SDVM is designed to examine.
Recent research argues that aggregate success metrics can hide critical operational failures, including inconsistency, poor robustness, unpredictability, and unsafe error behavior.
Towards a Science of AI Agent Reliability arXiv preprint
Enterprise analysis of the agentic era highlights that AI systems increasingly do more than produce outputs: they can trigger actions, use tools, and affect downstream systems, making governance and monitoring harder.
State of AI trust in 2026: Shifting to the agentic era McKinsey
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.
The public SDVM implementation is trace-first: structural workflow diagnostics, handoff friction, calibration readiness, map-gap handling, temporal reporting, and assisted PRE/POST/DELTA pilot analysis.
Observability answers: what happened? SDVM helps answer: where is coupling health degrading across cycles? In assisted pilots, SDVM starts from traces your existing tools already capture, then identifies whether additional capture is needed for a meaningful PRE/POST comparison. It adds a diagnostic interpretation layer—not an outcome replacement: where structural friction concentrates, 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. The full method addresses silent degradation; the current public build is strongest on trace-first structural signals.
SDVM does not replace tracing, monitoring or evaluation tools. It organizes structural workflow signals, repair accumulation, handoff friction and step deviation as a structured diagnostic report rather than a dashboard event stream—paired with outcome KPIs where available.
SDVM pilots are intentionally narrow. They begin with pilot scoping, observability readiness, and workflow mapping—then a trace-first diagnostic assessment to see whether structured trace diagnosis can identify structural 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. Assisted pilots remain 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 assisted-pilot path is 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. Semantic drift analysis requires richer Artifact X / Tier ≥2 instrumentation beyond trace-only capture.
The goal is assisted pilot readiness: diagnostic usefulness, report quality, evidence thresholds and tuning guidance under real or semi-real workflow conditions—not commercial validation of universal silent-drift detection.
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 observability readiness, trace capture, and a progressive instrumentation path—not plug-and-play drift detection.
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 assisted pilot work on AI-assisted workflows.
Jamhour also brings prior executive experience in institutional finance and risk-sensitive operations, including the Stanford Sloan Fellows program.