I'm going to say what everyone's thinking: if your system just searches documents and paraphrases results, it's not an agent. It's a search engine with a natural language interface.
You've traded precision for generalization and gained the thrilling possibility of hallucinations. But there's no agency, no autonomy, no ability to actually do anything beyond return text that might not even be right.
Don't get me wrong—I've built plenty of RAG systems. Hell, I've got a dozen still running in production at Fortune 500 companies right now. They have their place. They're useful.
But calling them agents is like calling a calculator a mathematician. It completely misses the fundamental point of what makes something an agent: agency.
The Real Difference
Real agents take actions. They don't just tell you about your calendar—they schedule the meeting. They don't just find the bug report—they create the GitHub issue. They don't just analyze your expenses—they file the expense report.
The technical leap from RAG to true agents isn't trivial:
RAG systems need:
- Vector databases
- Retrieval algorithms
- Embedding models
- A prayer that the context window is big enough
Agents need:
- Tool calling infrastructure
- Authentication systems
- Execution environments
- State management
- Error handling that doesn't involve apologizing
RAG systems fail gracefully by returning "I couldn't find that information." Agents fail by sending the wrong email to the wrong person at 3 AM.
Why This Distinction Matters
This isn't semantic nitpicking. It fundamentally shapes how we build.
RAG systems can run in simple request-response loops. Ask question, get answer, everyone goes home happy.
Agents need sophisticated architectures that handle:
- Long-running workflows
- Concurrent tool execution (and yes, parallel execution too—they're different and it bugs me)
- Complex decision trees
- State persistence across sessions
- Rollback mechanisms when things go sideways
I see teams waste months trying to stretch RAG systems into agent capabilities. They came in wanting what an agent could do and settled for a search engine with extra steps.
They add "features" like:
- Returning URLs the user can click (hoping they're logged into that browser)
- Generating code the user can copy-paste
- Creating draft emails the user can manually send
These aren't agent behaviors. They're workarounds for systems that can't actually act.
What It Takes to Graduate from RAG to Agents
The shift requires a fundamental architectural rethink:
Instead of just retrieving context, you need tool discovery and selection. Your agent needs to understand not just what information is relevant, but what actions are possible.
Instead of just generating text, you need to parse and execute tool calls. The logic comes from business goals, not just language patterns.
Instead of single operations, you need workflow management. Agents make decisions, handle branches, recover from failures.
Instead of god-mode service tokens, you need multi-tenant authentication and authorization. Every action needs to be scoped to the right user with the right permissions.
Most importantly, you need to accept that agents are orders of magnitude more complex than RAG systems. You're no longer dealing with hallucinations you can fix with a better prompt. You're dealing with systems that can take irreversible actions in the real world.
They require real software engineering, not just prompt engineering. They need:
- Testing frameworks that go beyond "does this sound right?"
- Deployment pipelines that handle more than model weights
- Monitoring systems that track actions, not just responses
- Rollback strategies for when your agent decides to be creative
The Payoff
Here's the good news: once you make this leap, the possibilities explode.
Your AI doesn't just tell users what to do—it does it for them. That's the difference between a helpful search and an actual assistant.
- Your support agent doesn't just find the refund policy—it processes the refund
- Your DevOps agent doesn't just identify the failing service—it restarts it
- Your sales agent doesn't just surface leads—it sends the follow-up emails
This is what's next, and it's fucking awesome.
But please, let's stop calling RAG systems agents. They're both valuable. They're both needed. But they're not the same thing.
Build RAG when you need better search. Build agents when you need things done.
Sam Partee is CTO and co-founder at Arcade.dev, where he's building the infrastructure that lets agents actually do things. He's implemented over 100 LLM applications and has strong opinions about all of them.
Want to build agents that act instead of just chat? Start with Arcade.dev or argue with me on Twitter.
