The Model Doesn't Matter; The Feedback Loop Does
The model didn't fix the bug.
The feedback loop did.
The model obsession
Teams spend weeks evaluating LLMs. They run benchmarks. They compare token prices. They debate Claude vs GPT vs Gemini in Slack threads that never end.
Then they pick a model, wire it into their workflow, and watch it fail on the third step of a multi-part task. The model suggested a fix. The fix didn't work. The model doesn't know the fix didn't work. It moves on to the next step, building on a broken foundation.
The model was fine. The workflow was broken.
What actually matters
The differentiator in agentic systems is not model selection. It's whether the system can detect failure, diagnose what went wrong, and correct before moving on.
"It's not the model: you could use pretty much any model, as long as there's ability to say, 'I'm doing a task. Did it work? If it didn't work, what went wrong?' and then feed back to the orchestrator or LLM to fix that, and only move to the next step once that task is fixed. That's the heart of basically any agentic system."
Rob,
This is the close-the-loop principle. The agent proposes a change. The agent runs a command to validate. The agent reads the output. The agent adjusts based on real results.
Without that loop, you're trusting the model's first guess. And first guesses compound. A wrong assumption in step two becomes a catastrophe by step five.
The TDD parallel
The structure that makes this work is familiar. It's the same structure that makes test-driven development work: write a test, watch it fail, make it pass, move on.
"When you look at agentic systems, what makes a really good agentic system? Orchestration is important, and the idea of tasks and subtasks is important, but ultimately it's a feedback loop: the feedback loop that you find within the structure of a sort of test-driven development, that is actually what makes autonomous development work."
Rob,
The test is the spec. The failure is the signal. The loop is the mechanism.
An agent that can run pytest, read FAILED tests/test_auth.py::test_refresh_token, and incorporate that output into its next attempt is fundamentally different from an agent that guesses, hopes, and moves on.
The tradeoff
Building feedback loops requires infrastructure. You need the agent to have execution access. You need approvals so you control what runs. You need the output piped back into context.
This is more work than "paste code into chat and hope." But the payoff is reliability. Tasks complete. Diffs match the failure mode. You stop being the human glue between the model and your terminal.
The model choice still matters for cost and capability thresholds. But once you're above that threshold, loop quality dominates. A mid-tier model with a tight feedback loop will outperform a frontier model that can't run the test.
Model-first vs. loop-first development
| Dimension | Model-first approach | Loop-first approach |
|---|---|---|
| Failure detection | Developer notices after copying code | Agent reads command output immediately |
| Error correction | Manual re-prompting with error text | Automatic iteration based on real results |
| Task completion | Stops at first suggestion | Continues until validation passes |
| Context retention | Lost between copy-paste cycles | Preserved in continuous task flow |
| Bottleneck | Human as feedback mechanism | Agent owns validation end-to-end |
How Roo Code closes the loop
Roo Code implements the close-the-loop principle directly in your editor. The agent proposes a diff, runs the command to validate it, reads the actual output, and iterates based on real results - not guesses.
With BYOK (bring your own key), you use any model that meets your cost and capability requirements. The model becomes interchangeable because the feedback loop handles the hard part: detecting failure and correcting before moving on.
An AI coding agent with a tight feedback loop will outperform a frontier model that can't run the test. The loop is the multiplier. The model is just the starting point.
Why this matters for your workflow
If you're debugging the same issue across multiple prompt attempts, the problem is probably not the model. The problem is that the model never saw the actual error output.
The shift: stop optimizing for "which model is smartest" and start optimizing for "can this workflow validate its own steps."
When evaluating agentic tools, ask:
- Can it run commands and read the output?
- Does it iterate based on real results, or does it guess and move on?
- Can you see the validation in the task log?
If the answer is "it suggests code and I copy it to my terminal," you're the feedback loop. That's the bottleneck.
Build workflows where the agent owns the loop. The model will surprise you.
Frequently asked questions
Stop being the human glue between PRs
Cloud Agents review code, catch issues, and suggest fixes before you open the diff. You review the results, not the process.