The Agent Control Plane Blog
Deep-dives on AI agent observability, runtime policy enforcement, FinOps token economics, and EU AI Act compliance architectures.
AI Agent Cost Attribution: Why Enterprises Can No Longer Afford Black Box AI Spending
Knowing your monthly LLM bill isn't enough. Organizations need to know exactly which agent, workflow, department, customer, and action generated every dollar of AI spend.
Runtime Policy Enforcement in AI Agents: Stopping Unsafe Actions in Real Time
Enforcement isn't just about writing rules—it's about stopping unsafe actions in real time, before the damage is already done.
Rethinking FinOps for Autonomous AI Agents
Traditional cloud FinOps was built for stable infrastructure. Autonomous agents demand a new discipline to track, attribute, and control dynamic costs.
What Is an AI Agent Control Plane?
An AI agent control plane discovers, identifies, authorizes, observes and stops AI agents at runtime.
Guardrails and Policy Enforcement for OpenAI Agents
Built with the OpenAI Agents SDK? Agentis captures guardrail spans automatically and enforces runtime policy with @govern before irreversible actions run.
Agentis vs Datadog: Choosing an Agent-First Control Plane
Datadog monitors your estate and adds LLM analytics. Agentis is the OpenTelemetry runtime control plane: observe agents, enforce policy at the agent boundary, and prove what happened.
Agentis vs LangSmith: Multi-Framework Governance vs Single Ecosystem
LangSmith helps you debug and ship agents in the LangChain ecosystem. Agentis helps you observe agents across frameworks, enforce policy at the agent boundary, and prove what happened.
AI Observability vs AI Governance: What's the Difference?
A one-question test to tell observability from governance, the EchoLeak case study, and why the gap is measured in milliseconds — not philosophy.
What Is AI Agent Governance? The Definitive Guide
Four planes of control, why observability alone isn't governance, and how to map your stack to the EU AI Act, NIST RMF, and ISO 42001.