Stop rebuilding agents. Start reusing them.

“Before your team builds another AI agent, do they know what already exists?”

This is the question many organizations are grappling with today as agents proliferate across individuals, teams, and whole departments.

The account management team has a LangChain agent that researches customers so they can better personalize their interactions. Meanwhile, the marketing team built their own OpenClaw agent that does the same thing so they can better market to their top accounts. Then the support team builds their Hermes version in order to better address customer issues.

These three departments built around different purposes with different paths, but ultimately got to the same answer because they didn’t have visibility into the rest of the organization’s capabilities. The result: duplicate agents solving variations of the same problem, each with their own maintenance, integrations, infrastructure, and cost.

Duplication creates more work; it also creates more inconsistency. If three teams build separate agents to answer the same type of question, those agents may use different models, prompts, data sources, or business rules and return different results. Reusing a shared agent helps give teams a more consistent answer and gives the company one place to improve that capability over time.

This is one of the many forms that agent sprawl adopts overtime, and it’s happening to organizations like yours right now.

If you can’t find it, you can’t reuse it

Software reuse isn’t a new idea. We reuse libraries instead of implementing every function ourselves, and we reuse APIs instead of rebuilding entire services. Companies create shared platforms because solving the same infrastructure problem over and over is expensive.

Agents should be no different, but first, people need to know what already exists.

IBM reports that only 18% of organizations maintain a current and complete inventory of their AI agents. This small percentage means some of the consequences of incomplete visibility into the agent environment – redundant solutions and limited knowledge transfer, according to IBM – are bound to multiply rather than shrink anytime soon.

AWS describes the same pattern across business units: teams independently build agents for functions such as procurement, scheduling, and reporting, often unaware another team has already solved the same problem. The sprawling consequences, according to AWS, are duplicate capabilities, hidden costs, and credential proliferation.

The solution seems simple: discover before you build. But that only works when there’s somewhere to actually discover those agents first.

Agents as reusable business capabilities

It’s easy to think about an agent as a standalone application. Maybe the application comes with a chat interface, or it’s attached to a UI at a specific URL, or maybe it lives inside of an existing workflow system.

Rather than thinking of it within the confines of a single application, we prefer to approach agents as a business capability instead.

A pricing agent knows how to calculate the right pricing for a deal. A customer research agent knows how to gather information about a prospect. A contract agent knows how to review an agreement. An inventory agent knows what is available and where.

Once those capabilities exist, there’s very little reason for every department to recreate them. A Sales application could use the pricing agent, a deal desk agent could call that same pricing agent, and a support workflow might use the customer research agent. Another agent could use all of those capabilities as part of a wider customer engagement workflow.

The capability (agent) gets built once and reused where it makes sense. It’s a massive shift (and reduction in work) from creating another isolated AI application every time someone identifies a new use case.

Discovery changes the economics of building

Let’s say a company has 50 agents created by 50 employees and employee 51 has a new problem to solve. The first step today is often to start building.

A better, more efficient first step is to search instead. Instead of building the fourth iteration of the same agent, user 51 can ask herself:

• “Does an agent already exist that can do this?”
• “Is there something similar that can be extended?”
• “Can an existing agent become one step in a new workflow?”

This is where an internal agent registry becomes important. Blocks Enterprise provides that private internal registry of agents. Organizations can connect agents within the company’s Blocks deployment, giving employees a place to see what capabilities already exist before creating another one.

Discovery doesn’t mean everyone automatically gets access, that could spell catastrophe for industry trade secrets and sensitive company info. With Blocks Enterprise, a finance employee might be able to discover that a useful Legal agent exists, but still needs permission to use it. A private agent can be shared with specific users or organizations instead, with revocability always at their fingertips if needed.

Reuse gets more interesting when agents can use other agents

Human discovery is only part of the story. Agents themselves can reuse other agents, too.

Instead of building every capability into one increasingly complicated agent, an agent can call a specialist. A deal desk agent might call a pricing agent for pricing guidance, a legal agent to review a contract clause, and a customer research agent for current account information.

Each team can continue improving its own area of expertise without every other team reproducing that logic. This starts to look less like a collection of isolated bots and more like a network of reusable business capabilities.

That’s an important part of the Blocks model: connected agents can be made available to people, applications, workflows, and other agents through the same private network.

Reuse what exists, build what’s actually new

The easier agents become to create, the easier it becomes to accidentally duplicate them, but that doesn’t mean teams should build and ship less. It means making the things a company has already built easier to find and reuse so teams can focus their time on what actually moves the needle.

Before starting agent 51, employees should be able to answer a simple question: Can one of the first 50 already do this?

If the answer is yes, use it. If the answer is almost, extend it. If the answer is no, build something new and make that capability available to everyone else who may need it next.

Stop rebuilding agents. Start reusing them.