Most

1. Your brokers remedy the identical issues repeatedly from scratch

If your agent handles related duties throughout periods and rebuilds its strategy each time from a clean immediate, you’re spending compute on work that already exists.

Modern architectures tackle this straight. When a brand new process arrives with out a related subagent, the system builds one and saves it for future use.

The subsequent time the same process seems, the groundwork already exists.

2. Successful runs go away nothing behind

In many frameworks, a profitable execution turns into a one-time occasion. The output is produced, logged, and forgotten.

If profitable runs are usually not creating reusable functionality, amassed data disappears after each session.

3. Your brokers get higher prompts however not higher tools

Prompt

4. Re-running a process sort requires rebuilding context each time

One of the hidden prices of static agent techniques is context overhead.

For instance, AgentFactory reduces this overhead by retrieving saved subagents for related duties, chopping down on repeated reasoning and setup work.

5. Your agent’s capabilities are tied to one platform

If your agent’s tools are tightly coupled to one framework or runtime, the work invested in building them stays trapped there.

Most of in the present day’s subagents may be exported and run in any Python-capable surroundings.

Portable, documented code turns a device right into a long-term asset.

6. Performance plateaus after deployment

A well-calibrated static agent usually performs strongly at launch after which ranges off, very similar to a New Year’s fitness center membership.

Without a suggestions loop that converts new duties into improved tooling, functionality development slows shortly.

If your deployment curve flattens after the primary few weeks, the absence of a self-improvement loop might be the rationale.

7. Scaling process quantity means scaling handbook upkeep

In many agent architectures, dealing with extra process varieties requires:

• More immediate engineering
• More configuration
• More human oversight

What you ought to be wanting to do is reverse that relationship.

As process quantity will increase, the subagent library ought to develop alongside it. A rising share of duties can then be dealt with by retrieving and adapting current subagents.

The system scales its own capabilities.

What this implies in follow

This shift will not be incremental. It introduces a unique psychological mannequin for profitable agent techniques. Instead of a pipeline that runs duties, you want an infrastructure that learns from them.

For groups building agentic AI systems, the sensible takeaway is easy:

• Static brokers are a place to begin
• Reusable functionality compounds worth over time
• Systems enhance quickest when each run produces one thing reusable

The most succesful agent techniques is not going to merely full duties. They will construct a rising library of confirmed, moveable capabilities that enhance with each process they encounter.

Of course, self-improving agent techniques include tradeoffs. Persisting and modifying executable tools introduces questions round verification, safety, model management, and failure propagation. 

A system that may enhance its own tooling may reinforce unhealthy patterns if the suggestions loop is poorly designed.

Final thought

Static brokers ultimately hit a ceiling. Systems that create reusable tools, refine them via suggestions, and carry these enhancements ahead function very in a different way.

Every accomplished process turns into a part of the system’s future functionality as an alternative of disappearing into the void 5 seconds later.

The long-term winners in AI will not be the techniques that sound the neatest in a demo. They will be the ones that quietly get higher each week whereas no person is wanting.

Source

Zhang, Z., Lu, S., Qian, H., He, D. and Liu, Z. (2026), “AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse”, arXiv:2603.18000. Published 18 March 2026.