Audit and govern
production AI agents.
Most enterprise AI agents in production have never been independently reviewed. Vector Systems delivers two-week audits and ongoing governance frameworks — built on the rigor regulated industries demand, applied to any enterprise running agents at scale.
Five Questions
Answer these about the AI agents you run in production. If you cannot, an audit is the next step.
When your agent makes a decision in production, can you explain why — with evidence — to a board member, a regulator, or a customer?
What is your agent's failure rate? Do you measure it, or do you find out when users complain?
If your agent hallucinated a tool call, exfiltrated data, or escalated outside policy — would your team know within minutes, hours, or never?
What evidence of human oversight could you produce if asked tomorrow? Logs are not an audit trail.
If the model provider changes the underlying weights next quarter, how will you know your system still performs?
Two Engagements
The audit finds what is broken. The governance framework keeps it fixed.
AI Agent Audit
Two-week structured assessment of a production agent system. Architecture review, failure mode testing, output quality, observability gaps, governance risks. Written report. Severity-scored findings. Fixed fee.
- Reasoning chains, tool use, memory, escalation paths
- Adversarial testing against the specific deployment
- Regulatory exposure mapping where applicable
- Remediation roadmap with effort estimates
Governance Framework
Ongoing infrastructure that keeps agents reliable as they scale, change, and accumulate edge cases. Standards, evaluation pipelines, monitoring, and human-oversight architecture. Quarterly review cadence.
- Automated evaluation of reasoning, tool use, goal alignment
- Decision-level logging — what was decided, on what evidence
- Human-in-the-loop checkpoints at defined risk thresholds
- Continuous regression testing against drift
Post-audit build work — remediation of specific findings — is available selectively and scoped to what the audit identifies.
How the Audit Works
Two weeks. Independent. Built to survive board, customer, or regulator scrutiny.
[ WEEK 01 // ARCHITECTURE REVIEW ]
We map your agent system from input to action. Topology, model selection, prompt design, RAG sources, tool access, memory and state, escalation logic. We identify where autonomous behavior exceeds intended scope before testing begins.
- Agent topology and decision pathways
- Tool access surface and permission boundaries
- RAG sources, retrieval logic, citation tracking
- Memory, state, and multi-agent handoff integrity
[ WEEK 02 // FAILURE MODE TESTING ]
We test against the specific attack surface of your deployment. Adversarial inputs, prompt injection, hallucinated tool calls, cascading errors in agent chains. Output reliability under load. Guardrail effectiveness against your real-world traffic.
- Adversarial input testing and jailbreak attempts
- Tool-call validation and data exfiltration paths
- Output quality under load and edge cases
- Cost, latency, and behavior drift over volume
[ COMPLIANCE MAPPING ]
Findings cross-referenced against the regulations applicable to your sector and jurisdiction. Most technical audit firms cannot do this. Most compliance consultancies cannot do the technical work. We deliver both — evidence-ready documentation in formats compliance teams can submit directly.
- EU AI Act Articles 9, 14, 15 where in scope
- FINMA Circular 2023/1, FCA AI guidance, FinCEN, HIPAA, SOC2
- Evidence packages for internal model risk review
- Jurisdiction-specific mapping by deployment type
[ DELIVERABLE // WRITTEN REPORT ]
25–40 page written report. Severity-scored findings. Remediation roadmap with effort estimates. Executive summary for board or regulator presentation. Built to be acted on within a week of delivery.
- Critical, high, medium, low severity by finding
- Remediation roadmap with effort and priority
- Executive summary suitable for board distribution
- Optional walkthrough with the technical team
{ "firm": "Vector Systems LLC", "services": ["AI Agent Audits", "Governance Frameworks"], "verticals": ["Enterprise", "Financial Services", "Legal Tech"], "principal": "Ex-Credit Suisse AVP // CMU Scholar" }
Running agents in production?
Request an AuditSelected Engagements
Production agent systems built and validated. The technical depth audit clients draw on.
The Executive Bio
Charles Camp
Carnegie Mellon research scholar. AWS Solutions Architect Associate. Five years operating independently across legal, financial, healthcare, and enterprise technology firms in the US and Europe — a breadth of production failure patterns most specialists in a single domain never see.
The audit and governance practice exists because most AI agents deployed in production have never been independently reviewed. Internal teams lack the adversarial perspective. External auditors lack the technical depth. Vector Systems provides both.
The Vector Standard (Guiding Principles)
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[ 01 // INSTITUTIONAL-GRADE RIGOR ]
The Origin: Tier-1 banking standards. The Application: Every audit is conducted as if the findings will face regulator scrutiny — because in regulated sectors they do, and in every other sector they should.
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[ 02 // ADVERSARIAL BY DEFAULT ]
The Origin: Production failure patterns across five years of agent work. The Application: We test what the agent does, not what the documentation says. Adversarial inputs, hallucinated tool calls, cascading errors. The audit assumes failure and proves resilience.
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[ 03 // VERIFIABLE LOGIC ]
The Origin: The bridge between accountability and code. The Application: Every agent decision traceable to source. Immutable logs at the decision point. Human-in-the-loop checkpoints at defined risk thresholds. Designed to survive post-incident review.
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[ 04 // FINDINGS OVER THEATER ]
The Origin: Five years of direct partnership with founders and operators. The Application: Reports are short, scored, and actionable. No 80-page checklist deliverables. The buyer should be able to act on the audit within a week of receiving it.