Designing Software for AI Agents

From making apps, browsing the Web, to creating files, today's AI agents today can take on an increasing number of computing tasks on their own. But the software underlying these capabilities, wasn't made for agents. It was designed and built for people to use. As such there's an opportunity, and perhaps an increasing need, to rethink these systems for agent use.

When building agent-based AI applications, you'll likely butt up against a number of situations where existing software isn't optimized for what thinking machines can do. For instance, Web search. Nearly every agent-based AI application makes use of information on the Web to get things done. But Web Search APIs weren't written with agents in mind.

They provide a limited number of search results and a condensed snippet format that lines up more with how people use Web search interfaces. We get a page of ten blue links and scan them to decide which one to click. But AI agents aren't people. Not only can they make sense of many more search results at once, but their performance usually improves with larger document summaries and contents. People on the other hand, are unlikely to read through all search results before making a decision. So search APIs could certainly be rethought for agents.

Similarly, when agents are developing applications or collecting data, they can make use of databases. But once again databases were designed and built for people to use not AI agents. And once again they can be rethought for agents, which is what we did with our most recent launch: AgentDB.

Agents can (and do) produce 1000x more databases than people every day, so the process of spinning up and managing any database for an agent needs to be as easy and maintenance-free as possible. Most of the databases AI agents create will be short-lived after serving their initial purpose. But some databases will be used again and others still will be used regularly.

With this kind of volume costs can become an issue, so keeping that many databases available needs to be as cost effective as possible. Last but not least, the content of databases needs to work well as context for AI models so agents can use this data as part of their tasks.

AgentDB is a database system designed around these considerations. With AgentDB, creating a database only requires a Universally Unique Identifier (UUID). There's no setup or configuration step. So whenever an AI agent decides it needs a database, it has one simply by creating a UUID. No forms or set-up wizards involved.

Databases in AgentDB are stored as files not hosted services requiring compute and maintenance. If an AI agent needs to query a database or append to it, it can. But if it never needs to access it again, the database is just a file. That means you're only paying for the cost of storage to keep it around and because AgentDB databases are just files, they scale. Meaning they can easily keep up with the scale of AI agents.

To make data within each AgentDB database easily accessible as context for AI models, every AgentDB account is also an MCP server. This makes the data portable across AI applications as long as they support MCP server connections (which most do).

Altogether this example illustrates how even the most fundamental software infrastructure systems, like databases, can be rethought for the age of AI. The AgentDB database system doesn't look like a hosted database as a service solution because it's not designed and built for database admins and back-end developers. It's built for today's thinking machines.

And as agents take on more computing tasks for people, it won't be the only software made with agents as first class users.