Adopt AI agents in regulated engineering—with adherence you can prove
Autonomous coding agents are joining regulated engineering teams. PEDCO AuditPro will keep your QMS explicit, current, and verifiable—so humans and AI agents work within the same guardrails, and adherence stays provable.
The challenge
Agentic engineering makes implementation fast and cheap—and amplifies whatever process it finds. Agents cannot read between the lines of a quality manual: undocumented practices, outdated procedures, and implicit decision boundaries turn into inconsistent, non-conformant work products faster than any team can review manually.
PEDCO AuditPro will make your process landscape explicit, agent-ready, and continuously verified.
What PEDCO AuditPro will enable
Assess: is your QMS agent-ready?
- Analyze whether processes, templates, and working agreements are explicit and current enough to guide autonomous agents
- Surface undocumented practices, contradictions, and stale content before agents amplify them
- Make decision boundaries explicit: which decisions agents may take, and where humans stay in the loop
Compile: guardrails humans and agents share
- Turn quality manuals and policies into structured, machine-consumable rules and expectations
- Keep every rule traceable to the clause and claim it comes from
- Serve process context—templates, quality expectations, boundaries—to your engineering tools
Verify: continuous adherence for every work product
- Check that required work products exist, follow their templates, and carry the right approvals
- Verify consistency across artifacts and systems of record—regardless of who or what produced them
- Trace every deviation to the expectation, claim, and clause it violates
Trust: provenance for agent-generated work
- Record which tool, version, and pipeline produced each artifact
- Verify that work products were generated by approved tooling
- Consume CI gates and workflow verdicts as evidence—enforcement at the source outranks after-the-fact inspection
How it will work
From documented process to continuously verified engineering—for humans and agents alike
One set of guardrails
The same rules govern people and agents
Evidence as work happens
Not reconstructed after the fact
Traceable to the clause
Every finding links to the requirement it violates
Six levels of verification
From mechanical checks that run on every artifact to deep content review where it matters—most of the ladder is deterministic; AI judgment is used where it adds value.
Completeness
DeterministicEvery required work product exists for each project or release—in the right place, on time, with the required approvals. Even when file names are messy: "RA_ProjX_v3_final2.docx" is recognized as the risk assessment.
Template conformance
DeterministicEach work product follows its template: required sections, tables, and fields—checked automatically, and re-checked when templates change.
Cross-artifact consistency
Deterministic + graphArtifacts agree with each other and with your systems of record: every hazard has a mitigation, test results cover the specification, ticket states match what documents claim.
Content quality
AI-assisted, sampledIs there a real risk analysis under the "Risk analysis" heading—or boilerplate? Judged against the template's expectations, sampled and scaled by severity.
Timing & approvals
Metadata-based"Approved before work starts", "reviewed annually"—verified from system-of-record timestamps and workflow events, which are hard to fake retroactively.
Generator provenance
Attestation-basedFor agent-produced artifacts: which tool, version, and pipeline produced them—verified against your approved-tooling policy.
Every check follows the platform-wide verification principles — how we verify
One deviation, end to end
What continuous adherence looks like in practice
Your process sets the expectation
"The risk assessment is approved before implementation starts." PEDCO AuditPro compiles this sentence into a checkable expectation—linked to the clause behind it.
Work happens—humans and agents together
A team starts a new feature. Code, documentation, and tickets flow into your repositories and systems of record.
Checks run as work happens
The risk assessment exists and follows its template—but the approval came three days after the first commit. A finding appears the same day:
Approval recorded after implementation start
- Deviation
- Approval 3 days after the first commit
- Expectation
- Risk assessment approved before implementation starts
- Source
- Risk Management Procedure, section 4.2
- Clause
- Automotive SPICE MAN.5 (Risk Management)
Reviewed, fixed, prevented
Your quality lead confirms the finding, the team fixes its workflow, and a CI gate now enforces the approval order—verified automatically from the next release on.
How teams will use it
For Quality & Compliance
- Prove process adherence as AI-generated work products multiply
- Replace manual sampling of project folders with systematic verification
- Defensible, evidence-backed findings for internal and external audits
For Engineering Leadership
- Adopt agentic tooling without losing architectural and process control
- Explicit decision boundaries: what agents may do, where humans stay in the loop
- See where executed reality drifts from the designed process
For Executives
- Scale engineering throughput without scaling compliance risk
- A decision-ready view of process health across programs
- Confidence that governance keeps pace with AI adoption
UI + API (integrated, not separate)
Use the UI to govern and report
- • Dashboards of process adherence across teams, projects, and agents
- • Evidence-backed findings with full traceability to clauses and claims
- • Export-ready reporting for audits and governance forums
Use the API to meet agents where they work
- • Feed process context and quality expectations into your engineering toolchain
- • Trigger adherence checks from CI/CD and workflow gates
- • Keep agent-produced work products inside the same governed evidence flow
Outcomes you can expect
A QMS that guides autonomous agents instead of confusing them
Continuous adherence: deviations surface as work happens, not months later
One governance model for human and AI-generated work products
Faster, safer adoption of agentic engineering in regulated programs
Less manual sampling, more systematic verification
Audit-ready traceability from every finding to the clause it violates