Context engineering enables AI agents to “think” more like humans – with memory, continuity and relevance.
We’re at the brink of a major shift in AI. What began as simple, task-specific models is now evolving into something far more powerful: multi-turn, reasoning-driven agents that can plan, act and adapt. In this new era, context isn’t just useful – it’s the bedrock on which meaningful interactions are built.– the emerging discipline of designing and delivering the right information at the right time to AI systems.
Then there’s memory: persistent storage of long-term facts or user preferences that lets agents personalise responses and “remember” over multiple sessions. Finally, tool use and function calling allow agents to trigger specific actions – booking a meeting, querying a database, even running code – in the middle of a conversation.
As foundational as context engineering is, it’s not without its challenges. The most pressing include:: Most large language models operate within a fixed context window. If prompts or data inputs exceed that limit, critical context can be lost or truncated – leading to degraded performance.: When LLMs lack clear or accurate context, they can fabricate information, creating misleading or entirely incorrect outputs. This often happens when irrelevant or conflicting context is introduced.
The system retrieves the relevant legal context, such as jurisdiction-specific laws or previous interactions with the user.It sends this full context – not just the raw prompt – to the model, wrapped in task-specific metadata.
AI Agents BBD Openai Retrieval-Augmented Generation
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