Cloud & Infrastructure
Pragmatic architecture, migrations, reliability, and cost-aware operations built around the business—not the tooling.
I help leaders turn cloud, security, DevOps, and emerging AI into systems that are resilient, responsible, and ready for the real world.
Research terminology: on this site, an AI agent harness is the vendor-neutral control and execution environment around models and agents: context, tools, permissions, state, decision gates, evidence and effects. It does not refer to Harness.io.
Start with the complete L0–L8 architecture, then choose a practical security or GRC path.
Connect purpose, portfolio, runtime, context, tools, security, evaluation and execution through one access-review example.
Open the Knowledge Hub ↗How to secure AI agents across tools, identity, context and memory. A practical control checklist with failure tests and links to detailed architecture.
Read the overview ↗A practical introduction to AI-assisted governance, risk and compliance: evidence collection, control mapping, human decisions and a bounded pilot.
Read the overview ↗Five source-attributed findings from verified Harness Collection PRs, with implementation limits.
Read the latest article ↗L5 · Applied controls · 5 Nov 2026Skills, MCP and Tools: From Discovery to Governed InvocationQualify capabilities, enforce per-action authority and verify effects.
Read article ↗L5 · Architecture · 3 Nov 2026Capabilities Are Not PermissionsWhy discovery and connectivity never grant authority to act.
Read article ↗Harness Weekly · 24 Oct–1 Nov 2026Harness Weekly: Evidence Before EffectsMonitoring, identity, skills, security evidence and privacy controls from the 1 November update.
Read article ↗Vendor-neutral evaluation of AI agent platforms, controls and deployment evidence. Compare the options in your environment, challenge their boundaries and document an accountable go/no-go decision.
Compare interoperability, deployment constraints, security, privacy and total cost.
Define authorized negative tests for tools, identity, permissions and fail-safe behavior.
Record observed results, gaps, ownership, residual risk and a defensible next step.
From strategic decisions to the operating details that make them work.
Pragmatic architecture, migrations, reliability, and cost-aware operations built around the business—not the tooling.
Delivery systems that make secure defaults, clear ownership, and operational feedback part of everyday engineering.
Risk-led security programs, DPO perspective, resilience planning, and controls that teams can actually sustain.
Least privilege, human approval, auditability, and practical guardrails for AI systems that can take action.
The strongest technology decisions happen when business context, operational reality, and risk are considered together. That’s where I work best: across the boundaries.
Translate technical choices into cost, risk, speed, and resilience.
Build ownership and controls in from the start—not after launch.
Choose technology for the outcome, not for the trend cycle.
Independent advisory grounded in practical protection, cloud security, and accountable AI.