Over the past few months, I've experimented with quite a few enterprise AI projects.
One thing has become obvious.
Most teams are still building AI agents the same way they used to build web applications.
Every new use case becomes another agent.
Another prompt.
Another knowledge base.
Another API integration.
It works at first.
But it doesn't scale.
Every Agent Starts Solving the Same Problems
Imagine a company with ten AI agents.
One helps Sales.
One supports Finance.
Another assists HR.
Another generates weekly reports.
They look different from the outside, but internally they're solving many of the same problems.
Each needs:
- authentication
- permission control
- business definitions
- access to enterprise data
- shared documents
- tools
- monitoring
Yet many teams implement these capabilities over and over again.
The result is duplicated logic that becomes harder to maintain every month.
*We Already Solved This Problem in Software Engineering
*
Traditional applications rarely implement infrastructure from scratch anymore.
Authentication is shared.
Logging is shared.
Monitoring is shared.
Configuration is shared.
Developers focus on business logic because the platform provides the rest.
I think AI engineering is heading toward the same architecture.
Agents shouldn't own everything themselves.
They should consume shared platform capabilities.
What Should Live Outside the Agent?
When I look at enterprise AI systems, I increasingly think the agent should remain lightweight.
Instead of embedding everything inside prompts, I'd rather separate responsibilities.
For example:
Context Service
Responsible for business definitions, trusted datasets, and reusable organizational knowledge.
Tool Registry
A single place where agents discover available APIs, SQL tools, search services, and enterprise systems.
Permission Layer
Every agent follows the same access policies instead of implementing its own authorization rules.
Memory Service
Shared long-term memory instead of isolated conversation histories.
Observability
One dashboard to understand how agents are performing, what tools they're calling, and where failures occur.
None of these capabilities belong inside an individual agent.
They're platform concerns.
Keep Agents Small
One lesson I've learned is that smaller agents are usually easier to improve.
When an agent focuses on a single responsibility, it's easier to test, debug, and replace.
The shared platform handles everything else.
Instead of creating increasingly complex prompts, we should be investing in better infrastructure.
The more reusable the platform becomes, the simpler every new agent is to build.
A Different Mental Model
I no longer think of an AI agent as an application.
I think of it as a runtime component.
It receives a task.
It requests context.
It discovers available tools.
It checks permissions.
It completes the work.
Most of the intelligence isn't inside the agent itself.
It's distributed across the platform supporting it.
Final Thoughts
Right now, building an AI agent has become surprisingly easy.
Operating dozens—or eventually hundreds—of them inside an enterprise won't be.
The organizations that move fastest won't necessarily build more agents.
They'll build better platforms for those agents to run on.
To me, that's where enterprise AI engineering is heading next.
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