Composio Just Solved A Huge Pain in Agentic AI

There is a growing debate about whether AI has reached a plateau. Regardless of where you stand on that issue, one fact is almost non-negotiable. Agents do not work. Or at least, they do not work the way we expect them to.

As someone who has spent years deploying AI agents, I frequently witness a frustrating pattern in both my own workflows and those of my customers.

The Agent Reality Cycle

Day 1:

• You provide the agent with a specific instruction.
• It attempts to navigate through your connected toolkit.
• It selects a tool (sometimes the correct one, sometimes not).
• It attempts to execute the task with varying degrees of success.
• You request a status update.
• It provides the update.
• If the stars align, it actually works.

Day 2:

If you do not intervene, you are likely destined to repeat this struggle. In the worst-case scenario, the agent might take an entirely different, incorrect path to solve the exact same problem.

The inconsistency is maddening.

The Solution: Moving Beyond the Basics

There are existing workarounds for these limitations. I have personally used a few methods:

• Building dedicated workflows: You can use tools like n8n, but this requires significant maintenance, deployment, and testing.
• Implementing Memory: You can try to force the agent to "remember" the correct procedure. This improves the workflow occasionally, but it is rarely foolproof.