Most developers treat LLM tool-calling as a linear process, but that is where your agent reliability breaks down.
When you allow an agent to call multiple tools in a single turn, the context window gets messy. The model often struggles to map output from Tool A to the input of Tool B, leading to hallucinated arguments or infinite recursion.
The fix? Shift from a single-turn "do everything" prompt to a ReAct (Reasoning and Acting) orchestration pattern.
Instead of asking the LLM to call a suite of functions at once, force a strict sequence: Thought, Action, Observation. Treat the output of each tool as a mandatory state update in your workflow.
If you are using LangChain or DSPy, implement a tool-use constraint that restricts the agent to one function call per turn. Force the model to pause, observe the result, and re-evaluate its plan. It increases latency slightly, but it decreases your error rate by an order of magnitude.
Reliability in agents isn't about better prompts. It is about tighter control over the execution loop.
How are you handling your agentic feedback loops?
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