Context Engineering

Connected points of light forming a network, representing the layers of modern AI engineering from prompts to agent graphs

Industry TrendsJuly 28, 20268 min read

The AI Engineering Maturity Model (Five Names From Prompts to Graphs)

The AI industry has named five engineering terms in four years. Prompt engineering came in 2022, context engineering in June 2025, then harness, loop, and graph engineering through 2026. The layers of work are real. Some of the names are marketing. Context engineering has frontier-lab backing. Harness engineering is gaining credible adoption. The two newest terms are still contested. A business leader should map problems to layers, not chase job titles.

Analytics and measurement dashboards on a laptop screen, representing the evaluation infrastructure behind reliable enterprise AI programs. Royalty-free image courtesy of Unsplash.

AI OperationsJune 26, 202614 min read

How AI Evaluation Quietly Became the New CX Differentiator

AI evaluation is the discipline of testing whether AI systems actually behave the way you intended. Most enterprises still treat it as an engineering afterthought. In 2026 that mismatch is the leading reason agent rollouts stall and governance reviews fail. Here is what real eval looks like and why CX leaders own a bigger share of it than they think.

A single self-contained AI agent on one side and a lead agent coordinating five subagents on the other, showing the choice between one agent and a coordinated team

Agentic AIJune 24, 202614 min read

When Do Multiple AI Agents Beat a Single Agent?

In June 2025, two leading AI teams gave opposite advice on multi-agent systems. Anthropic showed many agents beating one by 90 percent on research tasks, at about fifteen times the token cost. Cognition warned that splitting work across agents breaks context and creates fragile systems. For enterprise CX leaders, the real question is not how to coordinate agents but whether to, and what to build first.

Close-up of a circuit board, representing context engineering as the infrastructure layer for enterprise AI

AI StrategyJune 21, 202618 min read

Why Context Engineering Is the Infrastructure Enterprise AI Has Been Missing

Context engineering is the discipline of designing what information an AI system receives, how it is structured, and when it enters the model's working memory. Gartner named it the breakout AI capability of 2026 and predicts context improvements will enhance agentic AI accuracy by 30%. For CX leaders, context architecture determines whether AI investments deliver customer trust or operational failure.

Enterprise data dashboard showing multiple analytics panels, representing the visibility gap between AI deployment velocity and governance infrastructure

AI GovernanceJune 18, 202616 min read

The AI Control Gap and Why Leaders Answer for AI They Can't Control

Two-thirds of CIOs and CTOs are accountable for AI systems they don't fully control, per a June 2026 IBM study of 2,000 executives. Seventy percent say business teams deploy AI faster than IT can track. Organizations embedding control deploy 16 times more agents with 25 percent fewer incidents than those relying on manual governance. The AI Control Gap is structural and solvable, but only by treating governance as a deployment prerequisite, not a follow-on project.

Articles tagged “Context Engineering”

The AI Engineering Maturity Model (Five Names From Prompts to Graphs)

How AI Evaluation Quietly Became the New CX Differentiator

When Do Multiple AI Agents Beat a Single Agent?

Why Context Engineering Is the Infrastructure Enterprise AI Has Been Missing

The AI Control Gap and Why Leaders Answer for AI They Can't Control

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