AI Governance as a Discipline
Deploying AI agents without governance is not a strategy - it is a liability. As agentic AI moves from experiment to production in 2026, engineering organizations face a new class of risk: uncontrolled agent costs, unclear accountability, and compliance exposure in regulated industries.
I build the governance layer that transforms AI from a liability into a competitive advantage. This means agent audit trails, reliability SLOs, FinOps accountability frameworks, and human-in-the-loop policies that give CFOs, boards, and compliance teams the visibility they need to scale AI with confidence.
Governance is not a barrier to AI adoption - it is the foundation that makes sustainable AI adoption possible.
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Why Governance Defines AI Maturity
Accountability
Every agent action is traceable. I build audit trail systems that record what agents did, when, why, and with what outcome - giving compliance teams and boards the visibility they require for regulated AI deployments.
Reliability
Agents in production need SLOs just like microservices. I define uptime, accuracy, latency, and human escalation rate objectives - then build the monitoring and alerting that enforces them with the same rigor as any production service.
Cost Control
AI agent costs compound fast without visibility. I implement FinOps frameworks that track cost-per-workflow, cost-per-automation, and ROI per agent deployment - giving CFOs the financial accountability they need to approve AI scaling.
FinOps for AI Agents
CFOs now demand tangible ROI from AI investments. I build the cost accountability layer that proves it - tracking every dollar of AI spend against measurable business outcomes.
- Cost-per-workflow tracking: Every agent workflow is tagged and costed - so you know exactly what each automation delivers and what it costs.
- ROI dashboards: Real-time visibility into automation ROI, cost trends, and efficiency gains - board-ready reporting without the manual data assembly.
- Budget guardrails: Automated spend controls that prevent runaway agent costs while preserving the flexibility to scale high-ROI automations.
- Cost attribution: AI spend attributed to teams, products, and workflows - enabling chargeback models and informed investment decisions.
Agent Reliability SLOs
Production AI agents need the same reliability standards as production microservices. I define and enforce the SLOs that make agentic AI trustworthy at scale.
- Uptime SLOs
Agent availability targets with error budgets and automated alerting when agents fail to respond or produce malformed outputs.
- Accuracy SLOs
Task completion rate targets - measuring the percentage of agent tasks completed successfully without human correction or escalation.
- Latency SLOs
Response time objectives for agent workflows, ensuring agents meet the speed requirements of the processes they are embedded in.
- Escalation Rate SLOs
Human escalation rate targets - tracking how often agents need to hand off to humans, and alerting when escalation rates exceed acceptable thresholds.
Human-in-the-Loop Policies
Autonomous Action
I define which agent tasks can be executed autonomously without human review - typically low-risk, high-frequency, reversible actions like PR triage, dependency updates, and documentation generation.
Human Approval Gates
High-risk or irreversible agent actions require human approval before execution - deployments to production, access privilege changes, financial transactions, and patient-facing decisions in MedTech.
Escalation Workflows
When agents encounter ambiguity, low confidence, or edge cases outside their training, structured escalation workflows route decisions to the right human reviewer with full context preserved.
Audit Trails & Compliance
Regulated industries cannot deploy AI without comprehensive audit trails. I build governance frameworks specifically designed for FinTech, MedTech, and other compliance-heavy environments where AI actions must be fully traceable and defensible.
Every agent action is logged with full context: what task was executed, which tools were called, what data was accessed, what decision was made, and why. This creates the audit trail that satisfies SOC 2, HIPAA, and PCI requirements for AI-assisted workflows.
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