Context Engineering and ContextOps: The Missing Foundation for Scaling Agentic AI
Forrester, Monday, October 19th, 2026
Forrester defines context engineering and argues agents are only as good as the context they get, making it a production dependency.
Forrester argues that many agentic AI initiatives stall for the same reason: agents are only as effective as the context they receive.
As agents move from answering questions to planning and executing business processes, they need accurate, timely knowledge of business state, goals, constraints and history.
Forrester formally defines context engineering as designing, orchestrating and delivering structured, relevant and timely context so AI outputs are accurate, grounded and compliant.
The post describes the building blocks, including RAG for knowledge, function calls for live data and actions, memory management and unified context models, and calls for ContextOps to run them in production.