It seems like everyone is building AI agents right now, yet no one really knows what an agent is.
Picture this: you're on the road to work and need a way to prioritize and respond to your emails before you get to the office. An AI model can easily do this task, but first, you have to copy the email, paste it into the chat and prompt the AI model correctly, revise their draft a few times, send the email, and then repeat for all your other emails. At that point, you might as well just do this manually.
But imagine if there is an agent that can just natively read your emails, learn your voice, obtain all the context needed from your inbox/Slack/Notion, automatically respond to these emails on your behalf (or draft things for your approval), and even create calendar events based on their content. That could save you at least 5 hours a week.
However, in order for an agent like that to work, the underlying AI model(s) needs to have access to the correct tools that give it control over your email inbox, Slack, Notion, other work/productivity apps, and your preferred calendar.
TL;DR: Connecting an AI model to these tools is what transforms it into an AI agent, and Dedalus Labs is building the underlying infrastructure so any AI model can easily connect to any tool without 200 lines of code. Dedalus enables you to build production-ready agents that help you or your users save hours of time each week and get things done.
How do we define agents?
At Dedalus, we define AI agents using this simple formula:
Agents := Model(s) + Tool(s)
An agent is at least one model (the brain) connected to at least one tool (the body). Tools enable models to interact with the real world, and tools can be APIs, MCPs, skills, or a local function call.
This equation is at the core of every agent you build. An agent is a collection of models and tools, which you can use to complete tasks, and a multi-agent architecture is just a combination of agents, aka. combination of models and tools with scoped access and context that is situational.
When you integrate an agent to any product or external service, the agent acts as the execution layer for that product. You specify intent and give your agent goals and autonomy across multiple steps while the agent decides how to execute on that intent at every step and what tools to use.
Think back to the email example. Creating an agent here is quite simple. You can connect your AI model to tools like an email client, your calendar, a simple web search tool, and add a speech integration. Then, as you commute to work, you could have a conversation with your voice agent, allowing you to reply to emails, identify high value leads from inbound messages, add events to your calendar, and everything else we previously discussed, all without taking your eyes off the road. The agent decides what sequence of actions it needs to take and the tools it needs to call, freeing you up to do other things.
Each part of the equation is essential. Remove the tools and you have a text generator. Remove the model and you have an automation script. The combination of a persistent goal (intent), a reasoning engine (brain), and the ability to act via concrete tools (body) is what makes something an agent.
Agents are:
- Connected to tools so they can take actions, not just generate text.
- Software systems that use an AI model to decide what to do next to achieve a goal.
- Able to observe results and iterate (plan → act → check → adjust).
- Designed to be repeatable and reliable, not just clever once.
Agents aren't: