You’ve just cloned a massive open-source AI agent framework. You open your editor, stare at tens of thousands of lines of interwoven code, and immediately panic. You turn to your favorite AI coding assistant and type: “Explain the complete architecture of this project.” The result? A grand, sweeping summary that sounds impressive but is completely useless. It misses the actual mechanics, hallucinates connections, and leaves you more confused than when you started.
You feel frustrated. You feel stupid. But the problem isn’t you, and it isn’t the code. The problem is how you’re using the tool.
Expecting an AI to spit out a complete architecture map in one pass is a developer’s illusion of laziness.
We’ve been sold the idea that AI code analysis tools are smart enough to ingest an entire codebase and instantly hand you a perfectly drawn map. They can’t. Context windows have limits. When you force too much into a single prompt, the AI picks and chooses what to highlight, and you have absolutely no way of knowing which parts are reliable and which are fabricated.
To truly own an open-source project, you have to stop treating AI like an oracle and start treating it like a scalpel. You have to peel the onion, layer by layer, from the top down.
Take Hermes Agent, for example. If you ask an AI for its architecture, it will probably tell you it’s a chat interface connected to an LLM. That barely scratches the surface. The real complexity of an agent framework isn’t the AI model itself—it’s the orchestration.
The real complexity of an agent framework isn’t the LLM. It’s making six different entry points converge safely into a single loop.
Hermes Agent doesn’t just expose a web page. It has six distinct entry points. The CLI goes through hermes_cli.main into cli.py, completely bypassing the gateway to construct the agent directly in-process. The TUI uses JSON-RPC over stdio. The Web layer is a tangled web of PTY mappings, WebSocket sidecars, and event subscriptions. Then you have Desktop, Messaging platforms (like Telegram and Discord), and the ACP protocol.
If you just ask AI to summarize this, it will choke. Instead, you must guide it. Step one: ask it to identify only the top-level modules and their corresponding directories. Step two: find the entry point files. Step three: map the dependencies and call relationships. Step four: generate the architecture document. Crucially, in step one, you must explicitly forbid the AI from diving into source code details. If you let it ramble into the weeds too early, the output collapses into incompleteness.
Use your tools based on their strengths. Let Codex chew through massive file reads. Use Claude Code for structural design logic. Use Trae for visual tweaks and minor file edits. Don’t force one tool to do everything.
When you finally map out the entry points of Hermes Agent, you find they all funnel into one place: run_agent.py. This is the unified main chain. No matter where the user enters, the AIAgent takes over. It assembles the system prompt, context, memory, and the available tools for that turn, then calls the model.
If the model needs a tool, it enters the execution chain: model_tools.py dispatches to specific tools that might access the local terminal, file system, browser, or third-party APIs. The tool’s result is written back to the message history, and the model continues reasoning. This loop repeats until a final answer is reached. Meanwhile, state is saved, logs are recorded, and cron jobs run in parallel. It is a highly orchestrated engine, not just a simple text-in, text-out wrapper.
But with great power comes a massive security target. Hermes Agent is MIT licensed, stores data locally, and uses container hardening. It’s transparent. But it can control your terminal, your files, your browser, and even your smart home devices.
Open source means transparency, not safety. Transparency gives you the right to audit; it doesn’t absolve you of the responsibility.
When you integrate or extend these frameworks, you must be ruthless with authorization. You need to know exactly which tools are enabled and which are locked down. You can’t secure what you don’t understand. By deconstructing the architecture layer by layer, you transform a chaotic mess of code into a clear, actionable map. You stop relying on the AI to do your thinking, and you finally start owning the code you’re shipping.
FAQ
Q: Can't AI tools just read the entire codebase and figure it out?
A: They can read it, but they can't synthesize it perfectly across a massive context. They will summarize, hallucinate, and miss critical architectural nuances like hidden entry points and parallel state management.
Q: What's the practical takeaway for my daily coding?
A: It means you must break AI analysis into four steps: top-level modules, entry points, dependencies, and finally documentation. Never ask for source code details in the first prompt.
Q: Shouldn't we just wait for infinite context windows to solve this?
A: No. Infinite context doesn't solve comprehension. Architecture requires human-guided decomposition to understand how systems interact, not just a massive data dump of files.