agentic AI - Peter Jonathan Wilcheck
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Non-Human Identity AI Agents: Why Every AI Agent Needs Its Own Verifiable Credential

AI agents that interact with calendars, databases, or support ticketing systems do so using credentials such as API keys, service accounts, OAuth tokens, or certificates. Security teams refer to these as non-human identities (NHIs), which have become the largest identity population within most enterprises. Recent industry research indicates that the ratio of machine identities to.. Read more
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Scaling Agentic AI: Turning Successful Pilots Into Production Systems

Most companies have now deployed an AI agent that delivers impressive demonstrations. For example, a procurement agent can draft a request for quote in seconds, a support agent can resolve a tier-one ticket autonomously, and a finance agent can reconcile accounts overnight. The challenge is not the demo itself, but what follows: after the pilot.. Read more
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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

Most AI deployments through 2025 followed a simple pattern: the model recommends, a person decides, and someone else clicks the button. That pattern is breaking down. A growing share of production agents now skip the last two steps entirely for low-stakes work, evaluating a situation and executing the resulting action — sending the email, updating.. Read more
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Multi-Agent Orchestration Layers: What to Look for in AI Hosting

A single AI agent that books meetings or drafts emails is easy to host. The moment a business puts five or ten specialized agents to work together — a researcher, a coder, a reviewer, a compliance checker — hosting stops being about running a model and starts being about running a system. That system needs.. Read more
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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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AI Swarm vs. Swarm AI: Why the Word Order Matters

AI Swarm vs. Swarm AI: Why the Word Order Matters When people say “swarm AI” and “AI swarm,” they often sound as if they are naming the same thing. In casual conversation, they sometimes are. In technical and executive settings, however, the order of the words matters because it signals two different design traditions. Swarm.. Read more
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AI as a Colleague: Building the Agentic Team Around Your People

For most of the last two years, companies have talked about AI agents as productivity tools — something you switch on to type faster or summarize a meeting. That framing is aging quickly. As agents take on multi-step work, decide their own next actions, and collaborate across a project, the better analogy is not a..

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The AI Studio Strategy: Why Centralized AI Teams Beat Scattered Pilots

Scattered AI pilots rarely produce enterprise value. The AI Studio strategy concentrates talent, budget, and governance in one accountable hub that ships high-ROI workflows to production. A practical look at why centralization works and how to build a studio without overbuilding it.

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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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