Report2026 edition

The state
of context
engineering

We surveyed IT and AI infrastructure leaders on how they build, feed, and govern context for production agents.

Context is critical. Almost no one has built for it.

Nearly all of them say it’s the deciding factor in whether AI works. Almost none of them have built the systems to prove it.

94%say compounding system intelligence is essential for production maturity. Only 4% have achieved it.

69%of the most mature organizations still feed critical decisions with stale data. Even the leaders have unfinished work.

60%are, by their own account, ineffective at governing who and what can reach context. Only 58% are confident their context is well governed.

69%of enterprises now consider an agent that can’t navigate across systems more dangerous than one that hallucinates.

infrastructure illustration for the State of Context Engineering 2026

cloudInfrastructure illustration for the State of Context Engineering 2026

Context is becoming the infrastructure for production AI

Every major shift in enterprise software includes a window when the shape of the next paradigm is still malleable.

Context engineering is in that window right now. The decisions organizations make over the next year will shape what their AI systems can do over the next five years or longer.

What is context engineering?

Context engineering is the practice of deciding what an agent should see at each step of a task—including which facts, records, past interactions, and systems it should retrieve from—and delivering that information in a form the agent can use, current enough to trust and fast enough for production.

For an agent to be useful in production, the context it runs on has to meet four requirements:

Navigable

Agents must navigate connected records and entities, not disconnected text.

Fresh

An agent acting on stale data will act confidently and wrongly.

Fast

A single task can involve dozens of retrievals, and slow context breaks the workflow.

Compound

The system must get more useful with every use, not start from scratch.

79%Nearly four in five organizations say context is very important to their data and AI goals, and another 18% call it somewhat important.

73%agree that navigable context systems are becoming the line that separates experimental AI from production AI.

81%agree that the next phase of AI maturity will be defined by infrastructure that gains value as knowledge accumulates over time.

Red and pink connected blocks with a floating cloud

Most organizations are still improvising

maturity illustration for the State of Context Engineering 2026

81% sit at the two earliest stages, even as almost everyone calls context critical. Five stages separate improvisation from compounding context:

Ad hoc: Individual teams handle context manually via one-off prompts and informal data pulls. There is little to no shared infrastructure and governance, resulting in inconsistent outputs.

Exploratory: Teams use RAG pipelines and vector stores, but systems remain siloed with limited freshness, monitoring, or governance.

Standardizing: Context sources move into shared interfaces. Capabilities become reusable, with basic freshness and monitoring in place.

Managed: A defined semantic model and context graph support governed access, monitored service levels, decision traces, and managed memory.

Compounding: Context improves with use. It’s managed as a product, with clear ownership, a roadmap, and foundations teams can build on.

For organizations at the earliest stage, the gap is even more pronounced:

98%say context is critical to AI and data goals.

95%say they probably or definitely lack a reliable semantic data model.

94%say they probably lack a well-defined context graph.

56%say reaching production took much longer than expected.

Four gaps are keeping context from production

The gaps holding organizations back are structural. Until the foundations are firmly set, there’s a limit to what better models or prompts can achieve. Here’s what’s holding them back.

55%Lack a reliable semantic data model

54%Siloed vector stores limit agents

47%Lack a well-defined context graph

39%Ineffective at memory scoping

freshness illustration for the State of Context Engineering 2026

cloudFreshness illustration for the State of Context Engineering 2026

Production context has to be fresh & fast

Even when context exists and is accessible, it’s often too outdated or too slow to rely on when an agent is about to act. Latency is where mature and immature organizations most sharply separate.

Latency risk drops with AI maturity

Freshness follows the same pattern. 78% say proving context is fresh before inference is very important. 80% agree that event-driven synchronization is becoming the standard for enterprise AI.

92%Ad hoc organizations

12%Compounding organizations

37%Governance tooling

22%Persistent memory

19%Unified runtime architecture

You can’t govern what you can’t assemble

Governance is the barrier leaders name most often: 37% consider governance tooling the single biggest barrier to production-ready maturity.

See where you stand

Knowing context matters is one thing. Building for it is another. Explore the context maturity model and use our assessment to see where your organization stands.

Watch the webinar

What is context engineering?

For an agent to be useful in production, the context it runs on has to meet four requirements: