People building Agent projects often run into a frustrating puzzle: their demo Agents seem incredibly smart, but real-world implementations often feel strangely dumb.
The problem isn’t that large language models (LLMs) aren’t capable enough. It’s that we design APIs the way we always have—around CRUD (Create, Read, Update, Delete)—instead of the higher-level abstractions Agents need.
An Agent doesn’t just need APIs wrapped around data and methods. It needs actions aligned with user intentions. We’ve found the root cause is usually a mismatch in abstraction levels. Here’s how this mismatch plays out:
1. Intent Expression Mismatch
Human intentions are contextual, fuzzy, and dynamic. If you say, “Help me set up next week’s project review meeting,” there’s a lot implied—avoiding key conflicts, ensuring relevant materials are ready, inviting the right people, and more.
But traditional APIs just see: calendar.create_event() with parameters like time, place, and attendees. This oversimplifies human intent into rigid parameters, limiting Agent intelligence.
Agents need an “Intent Modeling Layer,” representing goals, constraints, preferences, and context. This preserves the richness of human intentions and allows smart trade-offs during execution.
2. Execution Strategy Mismatch
After receiving intent, an Agent has to translate it into actionable steps. Right now, we mostly do this in a technology-driven way: first check calendars, then notify via Slack, then update Notion. This reduces the Agent to a mere API orchestrator.
A goal-driven strategy would instead abstract the intent more flexibly: first confirm availability, ensure key participants attend, and then establish shared context. Each sub-goal can have multiple paths, letting the Agent choose intelligently based on context.
Agents need a “Strategy Abstraction Layer” with high-level execution modes like “Progressive Confirmation,” “Fallback Options,” and “Context Awareness.”
3. State Management Mismatch
Current APIs oversimplify state management into binary outcomes—success or failure. Real-world states are usually progressive, negotiable, and recoverable. A meeting can be “partially confirmed,” “conflicting,” or “pending adjustments.”
Agents need “Semantic State Management,” tracking not just data changes but the semantics of task progression. This lets Agents intelligently handle intermediate states rather than simply retry or fail.
4. Error Handling Mismatch
Traditional APIs handle technical errors (network timeouts, bad inputs). But Agents mostly face semantic errors—goal conflicts, resource competition, or unmet expectations.
When there’s a meeting conflict, it’s not enough to return a generic error code (like a 409). The Agent needs context—understanding conflict severity, participant priorities, and possible adjustments.
Agents need “Semantic Error Handling” so errors carry business context, enabling intelligent reasoning about solutions.
5. Tool Capability Mismatch
Traditional APIs expose low-level CRUD capabilities. But Agents need higher-level task-oriented capabilities—like “intelligent meeting scheduling,” understanding time zones, and resolving conflicts.
Agents require clear “Capability Modeling,” where APIs explicitly describe their behavior patterns, suitable contexts, and limitations. This avoids guesswork and allows intelligent tool selection.
How to Fix It?
All these mismatches occur because we’re still stuck thinking about building static systems. To unlock the full intelligence of Agents, we need to shift from CRUD-style thinking to cognitive abstraction.
Only when the API’s abstraction aligns perfectly with the Agent’s cognitive needs can we see genuinely intelligent behavior.
What do you think? We’d love to hear your thoughts.
About Maybe
Maybe is an AI-powered platform that lets you turn any problem into a personalized AI assistant, without the need for any coding or workflow configuration. Create intelligent helpers in minutes through simple conversation. If you can describe your problem, you can build your solution.
Website: https://maybe.ai/
X: https://x.com/hey_maybe_ai
Top comments (0)