Blog — all articles
Every opsagent article on agent security, MCP, governance, observability, evals, orchestration, and oversight — newest first.
What is an AI agent? A definition without the marketing
An AI agent is software that plans and executes multi-step tasks on its own: model, tools, memory, and an action loop. How it differs from a chatbot and from automation, what it's good for, and which risks to control from day one.
Read →Policy Orchestration for AI Agents: One Decision Layer, Not Rules Scattered Across Prompts
Rules spread across prompts drift and leave no audit trail. How a policy decision point and enforcement points at the tool boundary give AI agents one rulebook.
Read →Agentic engineering: building software with coding agents you can actually ship
What agentic engineering means in 2026 — developing software with coding agents like Claude Code, Codex, Cursor and Aider — where the line to vibe coding runs, what AGENTS.md and CLAUDE.md are for, what the METR study measured, and what has to be true after the merge for the result to survive production.
Read →Indirect prompt injection: detecting and defending against it in your agent pipeline
How to detect and contain indirect prompt injection in a production agent pipeline — content isolation, provenance, output filtering and the layered defenses that shrink the blast radius.
Read →Red teaming AI agents: building a red-team program in your organization
How to stand up an agent red-team program as a repeatable process — scope, cadence, roles, and how organizational red teaming of autonomous agents differs from a one-off pentest.
Read →Memory poisoning: defending an agent's memory in production
How to defend a production agent's long-term memory and retrieval store against poisoning — validation, provenance, segmentation and expiry that stop false data biasing future decisions.
Read →The OWASP Top 10 for Agentic Applications: mitigating each risk in production
A production playbook for the OWASP Top 10 for Agentic Applications: how to meet and mitigate each of the ten risks with concrete controls, guardrails and governance.
Read →Tool poisoning: when an agent's tools turn against it
How tampered tool descriptions and crafted tool outputs steer autonomous agents into harmful actions — and the validation, isolation and provenance controls that stop it.
Read →The EU AI Act timeline for autonomous agents
When each EU AI Act obligation actually bites — the Article 50 transparency duties have applied since 2 August 2026, and the next hard deadline for agent teams is the high-risk requirements on 2 December 2027.
Read →Excessive agency: the most damaging agent risk
Why over-broad permissions turn a small agent mistake into a major incident — and how least privilege, scoped tools and approval gates contain the blast radius.
Read →EU AI Act vs GDPR: two regimes your agents must satisfy
Why GDPR compliance does not cover the EU AI Act, where the two overlap, and what an autonomous agent has to satisfy under each.
Read →Writing a company AI policy that actually governs agents
A practical template for an internal AI policy — the sections that matter, how to cover autonomous agents and Shadow AI, and how to keep it from becoming shelfware.
Read →ISO 42001 for agents: an AI management system, step by step
What ISO/IEC 42001 requires, how it complements the EU AI Act, and a practical path to an AI management system that actually governs autonomous agents.
Read →MCP security: secure configuration, identity and gateway authorization
How to run the Model Context Protocol safely in production: an MCP gateway, authorization and scoping, and non-human identity controls that keep agent tooling under control.
Read →Agent observability: the five signals and the alert rules that watch them
What to measure on a production AI agent — outcome rate, cost per run, loop depth, tool errors and liveness — with copyable alert rules, the OpenTelemetry GenAI conventions that standardise the data, and two failures from operating a real portfolio.
Read →Non-human identity: governing the agents that act for you
Agents authenticate as machine identities, not people. Here is why non-human identity is now a core control — and how to inventory, scope and rotate it.
Read →The OWASP MCP Top 10: securing the protocol agents run on
A practical walkthrough of the OWASP MCP Top 10 — the security risks specific to Model Context Protocol servers — and the controls that address each.
Read →Zero-trust for AI agents: implementing never-trust, always-verify in production
How to operationalize zero-trust for autonomous agents and the tools and agents they call — identity, policy enforcement and segmentation that make continuous verification real in production.
Read →Agent cost control: FinOps for autonomous AI
Why cost is a first-class signal for agents, what actually drives the bill, and how token tracking, loop detection and budgets keep an autonomous system affordable.
Read →Evaluating AI agents: from evals to reliable production
How to evaluate autonomous agents you can trust — LLM-as-a-judge done right, the metrics that matter, and the CI/CD quality gates that keep agents from regressing.
Read →The production metrics that tell you an agent is working
Six metrics — completion, accuracy, hallucination, latency, cost and satisfaction — that turn an agent from a black box into a system you can actually run.
Read →Tracing AI agents with OpenTelemetry: spans, attributes and working code
How to instrument an agent with the OpenTelemetry GenAI conventions — invoke_agent and execute_tool spans, gen_ai.* attributes, a Python example you can run — and why a trace with a correlation id once saved this portfolio from rotating a healthy credential.
Read →Agent benchmarks: what SWE-bench, τ-bench and GAIA actually measure
The agent benchmarks worth knowing — SWE-bench Verified, τ-bench with its pass^k reliability metric, GAIA's human-vs-model gap — where leaderboard numbers mislead, and a reading checklist for the next score a vendor shows you.
Read →Agent memory architecture: short-term, long-term, episodic
How autonomous agents remember — the three kinds of memory, when to use each, and why governing the memory layer is both a capability and a security decision.
Read →Agent orchestration: LangGraph vs CrewAI vs AutoGen and beyond
A vendor-neutral comparison of the main agent frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK and Google ADK — and how to choose between single-agent and multi-agent designs.
Read →Regression testing for AI agents: stop fixing one thing and breaking three
Why a golden dataset and CI gates are the only reliable defense against silent agent regressions when models, prompts and tools change underneath you.
Read →LLM-as-a-judge: prompts, code and calibration that make it trustworthy
A working LLM-as-a-judge setup: a judge prompt template you can copy, a DeepEval G-Eval example, the three biases Zheng et al. measured and how to counter them, calibration against human labels, and the cases where a deterministic check beats a judge.
Read →Agent guardrails: the automated limits around autonomy
Input, output and behavioral guardrails form the safety layer that lets an agent act fast without acting dangerously. Here is what each does and how they fit together.
Read →The agent kill switch: stop conditions, safe state and a drill that proves it
How to design the stop layer for an AI agent — kill switch vs circuit breaker, trip conditions you can copy, what 'halt in a safe state' means under Article 14 of the EU AI Act, and what happens when an automation runs for weeks with no breaker at all.
Read →Context engineering: giving an agent what it needs, not everything
Why what you put in the context window decides an agent's quality, cost and safety — and the discipline of assembling the right context at each step.
Read →Graduated deployment: raising agent autonomy without incidents
A five-stage path — dry-run, read-only, simulation, staging, production — for giving an AI agent more autonomy only after evidence says it is safe.
Read →Human oversight and agent autonomy: HITL, HOTL, HOOTL
The spectrum of human oversight for AI agents — from human-in-the-loop to human-out-of-the-loop — plus guardrails, kill switches and a graduated path to raising autonomy safely.
Read →Shadow AI: the agents and tools running without your oversight
Why unsanctioned AI use is the most common governance gap — and how to bring Shadow AI into the light without driving it further underground.
Read →Single-agent or multi-agent? A decision guide
When to split a task across multiple AI agents and when one is enough — the real costs of multi-agent systems and a simple test for whether you actually need one.
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