Agentic AI assurance

Make Your Agentic AI Defensible

Audit the agentic workflows you deploy, vet, or rely on. Map cost to outcome, expose visibility gaps, and show whether the outcomes justify the spend.

See the method

Fixed scopeOne production workflow. No retainer.

NDA-firstRead-only access in your sandbox.

Yours to keepAudit report and remediation plan.

The gap

Shipping agents is easy. Defending their cost and outcomes is not.

01
AI spend rolls up to a single org-level total.An outlier hits and nobody can answer why.
02
Telemetry is collected. Insights are not.Decisions are made on guesswork.
03
Vendor demos pass. Production behavior diverges.Failure modes that didn't show in the pitch.

Who it's for

Four scenarios. One method.

Each produces an artifact you can defend.

Operator

Agentic AI in production. Real bills, missing visibility. The audit produces a report you can defend.

Orchestrator

Vetting a vendor's agentic capability. Third-party verification under realistic load.

Evaluator

Diligence on a company whose pitch is built on agentic AI. Unit economics not yet legible. The audit holds up in committee.

Underwriter

Pricing a fixed-price agentic contract. Cost model not yet bounded. The audit surfaces where cost diverges from outcome before signature.

Method

Audit. Remediate. Verify.

Every engagement runs the audit. Operators usually run the full cycle; orchestrators, evaluators, and underwriters typically stop at the report.

Phase 01

Audit

Always

Walk the workflow end to end. What your code emits. What it's tagged with. What dashboards leadership can actually use. What governance bounds the agent. Whether cost connects to outcome.

Duration: Scoped per workflow
Output: Audit report

Phase 02

Remediate

Optional

Close the gaps the audit surfaced. Tag the dimensions you weren't tagging. Build the dashboards leadership can act on. Set cost ceilings. We do the work or hand the spec to your team.

Duration: Scoped per gap
Output: Working fix

Phase 03

Verify

Optional

Re-run the audit. Confirm the gaps stayed closed. Produce the artifact your leadership can rely on quarter after quarter.

Duration: 1 week
Output: Re-audit memo

What we look for

Five lenses on visibility.

Most agentic deployments fail at the same five layers. We inspect each one and report what you have, what you don't, and which gap is producing the most cost-to-outcome drift.

01Code emission

Signals

Traces, spans, events at every agent decision and tool call. Are the answers being captured, or scattered across logs nobody queries?

02Attribution

Dimensions

Workflow, user, agent, tool, model, version. Stamped on emit, not reconstructed after the fact. The dimensions that aren't tagged are the questions you can't answer.

03Surfacing

Aggregation

Rollups that answer leadership's question, not raw spans. The CFO needs the answer, not the CSV.

04Guardrails

Governance

Ceilings, alerts, circuit breakers on cost and tool authority. A rogue run costs 100× a normal one. Guardrails are what make “we trust the agent” defensible.

05The mapping

Cost-to-outcome

The connection between dollars spent and the outcome each run produced. Cost is easy to measure; outcome is hard. The mapping is what every commitment depends on.

A real finding

An audit produces named, ranked, fixable items.

Every finding traces to a lens, a detection method, and a recommendation. Concrete enough that your team can ship the fix without us.

CriticalLens · Dimensionsfinding-014.md

AI spend has no attribution.

Your agent emits LLM call traces with workflow context. Tool calls do not. AI spend appears as a single org-level total. No one can answer “which workflow, which user, which agent” produced it. When an outlier hits, root cause requires manual log forensics. Cost ceilings are impossible to set per-dimension because the dimensions aren't stamped on emit. Tag at emit: workflow_id, user_id, agent_name, tool_name.

DetectionPer-call inspection of emitted spans. Aggregate cost by each dimension. Flag any dimension without attribution.

EffortSmall. A single wrapper at the orchestration layer that stamps standard dimensions on emit.

RecommendationBuild the wrapper during remediation, or hand the spec to your team. Re-audit verifies the fix held.

Engagement

One workflow. Fixed scope. Fixed fee.

We work in your sandbox. Read-only access through your preferred channel. Scope confirmed in writing on the kickoff call. The artifact is yours to keep.

Scope: One production agentic AI workflow.
Window: Typically 2-3 weeks.
Fee: Fixed. Scoped on the first call.
Access: Read-only, through your preferred channel.
Artifacts: Audit report and remediation plan. Markdown + PDF.
Remediation: Separate engagement. Never bundled.

FAQ

We already pay for an observability tool. Do we need this?

Observability tools collect data; they don't synthesize it into a decision you can defend. Most companies running agentic AI own the tool, but no one is producing the cost-to-outcome view. The audit fills that gap.

What frameworks and stacks do you cover?

The audit is stack-agnostic. We have run it against agents built on LangGraph, custom orchestrators, the Claude Agent SDK, OpenAI Assistants, and bespoke agent layers. The five lenses apply regardless of the underlying framework.

How do you handle access?

Read-only, through your preferred channel. We work in your sandbox. NDA-first; nothing leaves the engagement without written approval.

What happens after the audit if we want fixes built?

Remediation is a separate engagement, scoped to the gaps the audit surfaced. We build the fixes, or hand the spec to your team. Either way, a re-audit verifies the fixes held.

Audit one workflow. Make it defensible.

Book a 30-minute call. We'll scope it and send a proposal.