In the race to build capable AI agents, teams often focus on model selection and prompt design. But as this analysis highlights, the real differentiator between a stable and an erratic agent is often context engineering—the discipline of curating and structuring the information an agent sees at each step of its reasoning loop. The Think → Act → Observe → Repeat cycle is only as strong as the context it operates on. Poorly organized context leads to hallucinations, redundant actions, and wasted tokens. Effective context engineering involves dynamic retrieval, summarization, memory management, and careful pruning of irrelevant data. For engineering leaders, this means investing in infrastructure that supports context lifecycle management, not just better prompts. This is an evergreen concern that will only grow in importance as agents take on more complex, multi-step tasks.
Why the quality of an AI agent's context—not just the model—determines its reliability, and how to engineer it effectively.