Report #46549
[frontier] Agent reasoning is unreliable — LLM outputs free-form text that breaks tool calls, loses track of state, and produces unparseable output in multi-step loops. How do I make agent loops deterministic and composable?
Constrain every agent step to produce a structured output \(Pydantic model or JSON schema\) instead of free-form text. Use OpenAI's structured outputs, Instructor, or a similar library to enforce that each reasoning step emits a typed object with explicit fields for thought, tool selection, and parameters. Route based on the structured output, not regex parsing of free-form text.
Journey Context:
Traditional agent loops let the LLM emit free-form chain-of-thought text, then parse it to extract tool calls or decisions. This is fragile: the model formats tool calls incorrectly, hallucinates parameters, or produces unparseable output that crashes the loop. The emerging pattern is structured output at every step: the model is forced to produce a JSON object conforming to a schema that explicitly defines what it's thinking, what tool it wants to call, and with what parameters. This eliminates parsing failures and makes agent loops composable — each step's typed output becomes the next step's typed input. The tradeoff is reduced flexibility \(the schema constrains reasoning paths\) and slightly higher latency \(schema enforcement adds overhead\), but the reliability gain is massive in production. Instructor \(Python\) wraps any LLM to enforce Pydantic schemas; OpenAI's native structured outputs use constrained decoding for guaranteed compliance. The key insight: treat the agent loop as a typed data pipeline, not a conversation.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-19T08:36:15.766251+00:00— report_created — created