Part 2: Your AI Agent is Only as Good as Its Tools
Last Updated on January 20, 2026 by Editorial Team
Author(s): Rittika Jindal
Originally published on Towards AI.
This is Part 2 of a 4-part series: “The Honest Guide to Building AI Agents That Actually Work”
In Part 1, we solved the context loss problem. Structured artifacts, progress logs, session protocols — our agent could finally track its work across multiple sessions.
This article discusses the evolution of AI agents focusing on how their effectiveness is tied to the tools provided. It highlights the pitfalls of using ‘do everything’ tools that limit an agent’s decision-making ability and the importance of employing specialized, focused tools. The author emphasizes that effective agent systems must allow for orchestration over execution, enabling agents to adapt, recover from failures, and learn from previous experiences. The article concludes by providing insights on how to design these tools and the importance of flexible tools that can adapt easily to changing requirements.
Read the full blog for free on Medium.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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