Debugging Agent Flow: When Tool Choice Fails

We've all been there. You write the perfect prompt, define your schemas, and hit run. The AI nods enthusiastically, says "I'm on it!", and then... absolutely nothing happens.

Yesterday I spent 4 hours fighting a specific behavior in my custom agent loop. The goal was simple:"Search the web for the latest Ethereum price."

I was using a lighter-weight model via a GRS API, and despite passing explicit `tool_choice` parameters, the model kept hallucinating that it had performed the search. It would output:

"I have searched the web and found that Ethereum is currently trading at..."

But my logs showed zero network requests. It was lying to me. The specific error code wasn't even an error—it was a silent failure of the ReAct loop.

The "Aha!" Moment

I realized that certain models, when fine-tuned for chat, sometimes prioritize "being helpful" over "being functionally correct." The fix wasn't more prompting—it was a structural change.

I implemented a Manual Fallback Trigger. If the intent detection classifier sees "search" but no tool call is generated, I force the tool execution in the graph.

# The Fix: Forcing the tool call
if intent == "search" and not tool_calls:
    print("[System] Manually triggering search_web...")
    result = search_web(query=user_query)
    # Inject result back into context
    messages.append({"role": "tool", "content": result})

Why This Matters

As we build more autonomous agents, reliability is king. We can't rely on probabilistic models to be deterministic 100% of the time. We need deterministic guardrails.

This experience reminded me that AI engineering is 50% prompt engineering and 50% traditional systems engineering. You still need to handle edge cases, timeouts, and yes, lying APIs.

Has this happened to you? How do you handle "lazy" models? Let me know!