Context Drives AI Agents
There are two main reasons why agentic systems fail: the limitations of the LLM itself or problems with the context it operates in. While the choice of model matters, most failures come down to insufficient or poorly structured context.
The context consists of all the facts, constraints, and goals that are available to the agent at decision time. It is important to ensure the LLM operates with a well-structured and complete context. Without a complete and organized context, even the most advanced models will struggle to perform effectively.
Agentic design patterns can be seen as strategies for building better context for the system:
Planning: when you break the goal into smaller steps, you help the system stay focused and organized.
Tool use: using tools to gather and organize information helps the system better understand its environment.
Reflection: by learning from past actions, the system can update its context; this will make future decisions smarter.
Multi-agent collaboration: when agents share and refine their contexts, the whole system becomes more accurate.
The next time your agentic system fails, consider how you can improve the context to avoid similar issues in the future. Success in agentic systems comes down to their ability to adapt and refine their context over time.