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Tagged: governance
AI Architecture for Production Agents: Failure Modes and Control Layers
A layer-by-layer architecture guide for production agent systems: failure modes, guardrails, evals, observability, cost control, zero trust and governance.
12 min readAI Agents Negotiating Deals: What Walmart's Bots Reveal About Real Risk
Agent-to-agent payments are live. The danger is not a secret robot language but collusion and unchecked mandates, and here is how to decide what to delegate.
6 min readThe Runtime Graph, Not the Model, Is the Security Boundary for Agents
A late-August 2026 reading of the Hugging Face incident, the cyber defense letter, MCP's missing authentication and TRACE attestation: why agent containment lives in the runtime.
11 min readChoosing an AI Agent Development Partner: Questions Beyond the Demo
How to pick an AI agent development company in 2026: workflow scoping, integrations, permissions, failure tests, deployment visibility and who owns it later.
8 min readGoverned AI Agents: One Policy Model, Several Runtime Enforcement Points
How to control cost, model choice, data, tools and MCP calls at runtime: a governance envelope, a neutral policy sketch, fallbacks, guardrail limits and tracing.
9 min readWhy Coding Agents Like Claude Code Need a Governance Layer, Not Better Prompts
A prompt shapes one exchange, not a workflow. How a governance layer of rules, scoped context, authority limits and proof of done make Claude Code dependable.
11 min readFull-Stack Orchestration for Enterprise AI Agents: Six Layers Beyond the Model
Enterprise AI agents fail around the model: integrations, state, observability, governance and human handoffs. Six orchestration layers and a maturity check.
7 min readWho Owns an AI Agent at Your Company: Six Responsibilities and Their Owners
Who owns an AI agent at your company? With SR 26-2 silent on agents, six accountable roles, how to staff them from existing teams, and day-to-day running.
16 min readLLMOps in Practice: Evaluation, Observability, Cost and Governance for AI
What it takes to keep an LLM application reliable in production: prompt versioning, evaluation suites, tracing, hallucination metrics, cost tracking and governance.
9 min read
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