Prasad Kavuri

Board-facing AI leadership evidence

Enterprise AI Operating Model

A practical CAIO operating model for moving AI from pilots to governed production: portfolio discipline, risk controls, AI FinOps, and board-readable business outcomes.

Operating System

Strategy to portfolio

Translate board and executive priorities into an AI portfolio with clear owners, value hypotheses, adoption milestones, and stop/go criteria.

Governance by default

Ship eval gates, human checkpoints, drift monitoring, prompt-injection checks, and audit trails with the platform rather than adding controls later.

AI FinOps discipline

Make model choice, token spend, latency, and infrastructure cost visible enough for engineering, finance, and product leaders to make tradeoffs together.

Operating cadence

Run AI delivery through metrics that executives can inspect: risk posture, model quality, adoption, cost per workflow, incident patterns, and business impact.

AI Platform Maturity Model

Most organizations stall between single-agent pilots and governed production. This is the path from prompt engineering to an enterprise AI platform, and where this work sits on it today.

Prompt Engineering

Single-call prompting against a foundation model, no retrieval or tool use.

RAG

Retrieval-augmented generation with vector search grounds responses in owned data.

Single Agent

One agent with tool access and a defined task scope, no cross-agent coordination.

Multi-Agent

Coordinated agents (analyzer, researcher, strategist) handing off work with shared context.

Managed Agents

Agents run under a control plane: RBAC, spend limits, structured observability, human checkpoints.

Enterprise AI PlatformCurrent focus

Governed agent runtime at organization scale — identity, policy engine, prompt versioning, canary rollout, rollback, FinOps, and audit as platform primitives, not bolt-ons.

Framework Alignment — NIST AI RMF and ISO/IEC 42001

How the controls running in this portfolio's open-source code line up with the four NIST AI RMF 1.0 functions and the ISO/IEC 42001:2023 Annex A control areas. It shows how I structure AI governance so auditors and risk partners can trace a control to code. It is an alignment map, not a certification.

Govern

ISO/IEC 42001 · A.2 AI policies · A.3 Internal organization

  • Default-deny tool policy: every agent tool declares its required scope; unknown tools are refused
  • Separate identities for the requesting user and the agent service principal, both recorded on every call
  • Fail-closed secrets: credential signing stops in production when the secret is missing
  • Spec-before-code change process with human approval for security-relevant changes

Evidence: Tool policy · Change specs

Map

ISO/IEC 42001 · A.8 Information for interested parties

  • A2A Agent Card and auth.md publish what the agent can do, which scopes each skill needs, and how to get a credential
  • Golden scenarios enumerate the risk cases up front: over-threshold payments, duplicates, and poisoned vendor data
  • Every demo declares its execution mode (browser, server, or remote agent) in one registry

Evidence: Agent Card · auth.md

Measure

ISO/IEC 42001 · A.6 AI system life cycle (verification and validation)

  • Trajectory evaluation scores each agent version on tool order, tool precision and recall, outcome accuracy, and safety violations
  • Eval suites and coverage thresholds run in CI on every push
  • Gateway spans record tool, principal, decision, reason, and latency for each call

Evidence: Release gate · Trajectory eval

Manage

ISO/IEC 42001 · A.9 Use of AI systems · A.10 Third-party relationships · A.7 Data

  • Consequential actions pause for human approval: single use, time-limited, bound to what was staged, replays rejected
  • A candidate that violates a safety case is rolled back from canary automatically
  • Tool and remote-agent outputs are treated as untrusted and screened for injected instructions
  • Daily spend caps and rate limits on every AI route; structured PII is re-checked before any cloud handoff

Evidence: Flagship demo · Tool gateway

Board Signals

  • 200+ engineers led across US, India, and Europe
  • $8M-$20M annual engineering budget responsibility
  • $10M+ revenue launched from production AI platform work
  • 70% infrastructure cost reduction and 50% latency improvement signals
  • 13,000+ B2B customers enabled through platform-scale delivery
  • 14 production AI demos showing governance, routing, retrieval, evals, and agent controls

Controls In Practice

Governed agent platform (flagship)Governance controlsEval-gated qualityEnterprise control plane