Context engineering: why AI agents get confused in long dialogues and how to fix it

Developers of conversational AI agents with RAG, tools, and memory describe the same problem: the system works great on short dialogues, but after several dozen iterations the model starts confusing tools, pulls outdated calls into the response, and relies on an error that once ended up in the context. A new discipline is addressing this problem — context engineering.

How this differs from prompt engineering

The key idea is that a new, more successful prompt does not always solve the problem of agent degradation. The issue is not the formulation of the instruction, but what information exactly reaches the model before each next reasoning step. Prompt engineering works with the text of a single request, RAG — with pulling external data, MCP — with standardizing access to tools. Context engineering works at a higher level: it manages all the content of the model's context window at each dialogue step — what goes in, what is removed, and in what order it is presented.

• The problem does not appear immediately, but after several dozen iterations of dialogue with the agent
• Symptoms: confusion in tool selection, use of outdated calls, reinforcement of previous errors
• Context engineering differs from prompt engineering, RAG, and MCP as a separate discipline of context management

Why the agent starts to make mistakes

As the dialogue grows, old tool calls, intermediate results, and already outdated facts accumulate in the model's context window. The model does not distinguish fresh information from outdated information automatically — it perceives everything in the context as potentially relevant. If an error once ended up there, the model may rely on it in subsequent steps, amplifying the discrepancy with the actual state of the problem.

What does this mean

As AI agents transition from demo to long work scenarios with RAG, tools, and memory, context management becomes a separate engineering discipline — on a par with prompt design and model selection. Teams that establish explicit rules for context selection and cleanup before each step get more stable agents on long dialogues.