Context engineering is a crucial aspect of harnessing AI models’ potential. According to LangChain co-founder and CEO Harrison Chase, this field involves designing systems that allow large language models (LLMs) to interact more independently and effectively perform long-running tasks.
In traditional AI systems, models are constrained from running in loops and calling tools, which limits their ability to think critically. However, new harnesses specifically built for AI agents enable them to run autonomously and complete complex tasks.
LangChain’s customizable general-purpose harness, Deep Agents, is a game-changer in this field. With planning capabilities, virtual filesystems, context management, code execution, and skills management, Deep Agents can delegate tasks to subagents, work in parallel, and maintain coherence over long-running tasks.
To achieve this, agents are given access to file systems, allowing them to create to-do lists that they can execute on and track over time. The harnesses also provide agents with the ability to compact context at points where it determines is “advantageous.”
Chase emphasizes that harnesses should be designed so that models can maintain coherence over longer tasks, and be “amenable” to models deciding when to compact context. By giving agents access to code interpreters and BASH tools, flexibility increases.
Moreover, providing agents with skills as opposed to just tools loaded up front allows them to load information when needed. This approach enables models to write their thoughts down as they go along, essentially creating a transparent and explainable system.
Context engineering is about understanding what the LLM sees and how that differs from human developers’ perspective. By analyzing agent traces, human devs can put themselves in the AI’s “mindset” and answer questions like: What is the system prompt? How is it created? Is it static or populated?
When agents succeed, they do so because they have the right context. When they mess up, it’s due to the lack of context. Chase views context engineering as bringing the right information in the right format to the LLM at the right time.
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Source: https://venturebeat.com/orchestration/langchains-ceo-argues-that-better-models-alone-wont-get-your-ai-agent-to