Understanding Agentic AI
Foundational explainers for senior IT readers. Definitions, taxonomies, and mental models — anchored to primary sources, not vendor marketing.
Agentic AI is the operating mode where a language model uses tools, carries state across multiple steps, and acts toward a goal with some degree of autonomy — distinct from chat, distinct from generative AI in the narrow text-out sense, and distinct from the rule-based RPA that most enterprises already run. The clearest definitional cut is at AI assistant vs AI agent: where the line actually sits. A useful test: if the system can pause its own reasoning, call an external tool, read the result, and re-plan, it is an agent. If it produces text only and stops, it is an assistant.
The architecture of an agent worth deploying at enterprise scale rests on three primitives. Tool use via standardised protocols — see MCP for enterprise agent tooling for the de-facto Q2 2026 wiring layer. Agent-to-agent communication for systems where one agent delegates to another — the topology is walked at the A2A agent-to-agent protocol and operationalised at the multi-agent architecture playbook. And identity for the non-human actor doing the work, which is the load-bearing IAM extension at non-human identity for AI agents. Without all three, what enterprises ship is a chat interface with tool plugins, not an agent.
The operating-model shift for IT is sharper than the technology shift. Most enterprises that move into production discover that the cost stack runs several times the pilot estimate (the cost economics), that the failure modes are structural rather than incidental (why 88% of deployments fail), and that the right success metric is mean-time-to-detect anomalous agent behaviour — the framework is at MTTD-for-Agents, the readiness diagnostic at GAUGE, and the practical entry point for teams new to the work at the agentic AI readiness diagnostic.
The articles below extend each of those threads on a 30–90 day review cadence. Read in order if approaching agentic AI cold; read by topic if a specific decision is in front of you.
Read deeper
The articles below extend this pillar — each one tracks a single primary claim on a 30–90 day review rhythm.
Why AI productivity gains create workforce reduction pressure: the demand ceiling and the competitive trap
The argument that AI-driven productivity lets companies keep all their workers and simply produce more runs into two hard limits: consumer demand and competitive dynamics. Both constraints are structural, operating regardless of management intent, and both resolve in the same direction: fewer workers for the same revenue.
You're Scoring This on the Wrong Axis
The coverage of Karpathy joining Anthropic's pre-training team read it as a talent-war coup. It is also misreading which seat has the leverage. That axis error is one enterprise IT makes with its own best engineers every day.
97 percent invest, 5 percent are ready: why enterprise AI data readiness is a budget allocation problem
Dun and Bradstreet's 2026 AI Momentum Survey of 10,000 businesses across 32 countries found that 97 percent of organisations report active AI initiatives, but only 5 percent say their data is adequately ready to support them. That gap is not primarily a technology problem. Most enterprise data environments were built for human workflows, not for autonomous AI systems operating continuously across mission-critical processes. The gap between initiative volume and data readiness is a budget-allocation failure: enterprises that treat data infrastructure as the prerequisite spend rather than a parallel track are the ones that reach scale. Enterprises that treat it as a follow-on investment do not.
Single-agent or multi-agent: what the 2026 deployment record actually says
The 2025–2026 deployment record shows single-agent architectures win on accuracy, cost, and MTTD below roughly 12 tool-domains. Multi-agent only pays back above that threshold, and only when inter-agent state is bounded by a shared structured artifact.
The agent fan-out problem: when one prompt becomes 400 LLM calls
Production agentic systems amplify a single user request into dozens or hundreds of internal LLM calls. Most enterprise unit-economics, latency budgets, and observability setups are still priced for 1:1.
The retraining gap: what the surviving 70% need to learn after AI displaces 30% of a function
Enterprises planning the headcount-reduction half of an agentic-AI rollout are systematically under-budgeting the upskilling cost for the residual workforce. The skills the AI replaces are not the skills the survivors need.
Why this publication has a ledger — and the analyst sites it benchmarks against don't
The single structural feature that distinguishes this publication from every site a senior IT leader currently subscribes to is a public claim ledger. None of the named comparables — Stratechery, The Information, the Substack analyst stack, the Big-4 research blogs, Gartner, Forrester, IDC — maintain one. The reason is not negligence.
The AI-author signature decision: why this publication signs every piece 'Written by Claude · Curated and signed by Peter'
Five publishable byline formats exist for AI-authored enterprise commentary in 2026. Four are in active use across the analyst-publication category. This site picked the fifth, and the choice is the second-most-consequential editorial decision after the claim ledger.
When AI writes about AI: the case for tracked claims
Most enterprise-AI publications hide their AI use. A few disclose it. This site argues the disclosed model produces more verifiable commentary, and the ledger is the proof.
AI assistant vs AI agent: the procurement distinction
AI assistants and AI agents are not the same product class. One suggests; the other acts. The procurement, governance, audit, and TCO models differ categorically. Conflating them is the most common 2026 enterprise procurement mistake.
Why your agentic-AI deployment needs an AI Training Lead
The AI Training Lead — the human who curates training data, evaluates model outputs, and tunes prompts — has quietly become a budget-line for enterprise agentic-AI deployments. Domain experts tend to outperform pure-ML hires in the role. CIOs that do not budget for it see their projects fail at the integration boundary.
AI and jobs: why the task-level frame is the one CIOs need
The job-level question every CIO is fielding from employees — 'will AI replace my role?' — keeps missing what is actually happening at the task level. The frame mismatch is the visible mechanism behind the retraining-budget gap.