Continuous Governance-as-Code: Building Compliance Into the Agentic Architecture
Most AI governance programs were designed for software that changes only when someone ships a release. Agentic AI does not behave that way. An agent’s effective behavior is assembled at runtime from a model, a system prompt, a tool set, and retrieved context — and any of those can shift while the compliance team is..
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Non-Human Identity AI Agents: Why Every AI Agent Needs Its Own Verifiable Credential
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Non-Human Identity: Securing the Credentials AI Agents Use to Act
Every AI agent that takes real action is a non-human identity — a service account, API key, or OAuth token with permissions of its own. A practical look at secrets sprawl, over-privilege, and prompt injection, and how least-privilege, short-lived, revocable credentials make agent actions safe and accountable.
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Self-Healing Automation: How AI Agents Adapt When Interfaces Change
Self-healing automation lets AI agents notice when a UI element moves or changes, find it again on their own, and keep working. A practical look at the healing loop, three generations from locators to semantics, where agentic reasoning goes further, and what it still cannot fix.
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Autonomous Decision Execution: How AI Agents Take Action Without Waiting
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AI Governance and Compliance: Keeping Autonomous Agents Inside the Rules
A practical look at what it takes to govern autonomous AI agents in 2026: the EU AI Act’s compliance floor, the six control layers that turn policy into enforcement, and why audit trails — not guardrails alone — are what regulators and engineers actually rely on.
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Reasoning Models and Test-Time Compute: Letting AI Agents Think Before They Act
Reasoning models and test-time compute let AI agents generate a hidden chain of thought — planning, checking assumptions, and self-correcting — before they take action. A practical look at why smarter inference beats bigger models, the latency and cost trade-offs, and how to put reasoning agents into production without overspending.
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How Browser Agents Let AI Complete Multi-Step Web Tasks
Ask a chatbot to book a flight and it will happily describe how, then stop. Ask a browser agent the same thing, and it opens a tab, reads the page, clicks the right buttons, and comes back with a confirmation number. That gap between describing a task and finishing it is exactly what browser agents..
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Context Engineering: Why Relevant Context Beats Clever Prompts for AI Agents
For years, building with language models meant perfecting the words inside a prompt. That era is closing. As AI agents take on multi-step work—researching, calling APIs, writing files, waiting on human approvals—the decisive question is no longer “what should I write?” but “what information should enter the model’s attention at each step?” This is context..
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Small Language Models: Why SLMs Power Efficient AI Agents in 2026
For most of the last few years, building an AI agent meant wiring everything to the largest, most capable model available. That instinct is now being questioned. A growing body of research argues that for the repetitive, narrow tasks agents actually perform, a smaller model is not just adequate but often the better engineering choice…
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