DeepSeek-V3.2 is surprisingly stable in long tool-calling loops, mainly due to two factors: (1) improved tool-selection training, and (2) long-context handling via sparse attention. Because V3.2-Exp is distilled from specialist agents—including an “agentic coding” specialist and an “agentic search” specialist—it has more robust behavior when deciding whether to call a tool, which tool to call, and how to format parameters. In multi-step workflows, it maintains awareness of earlier calls across many turns, making fewer redundant or contradictory requests compared with earlier models. That said, stability always depends on how well you structure prompt templates, schemas, and error-handling logic.
In practice, the biggest failure mode in long loops is schema drift, where the model gradually deviates from the expected function argument format. DeepSeek-V3.2’s JSON mode and stricter tool-calling interface mitigate this somewhat, but developers still report occasional field omissions or value-type errors in 15–30 step loops. You can counter this by implementing automatic schema validation and letting your orchestrator return structured error messages that the model can correct. With this pattern, V3.2 reliably fixes its own malformed tool calls and continues the workflow without losing context or switching strategies mid-way.
When retrieval is part of the loop—such as when querying Milvus or Zilliz Cloud—you get additional stability by separating concerns. Let one tool handle semantic search, another handle metadata fetches, and another handle updates or Logging. This keeps arguments small and predictable, reducing surface area for mistakes. DeepSeek-V3.2 is particularly strong at iterative refinement: for instance, it might search a collection, inspect the results, and issue a follow-up search with narrower filters. In long-running automation tasks (e.g., multi-step data cleanup, code refactoring, research workflows), this leads to cleaner loops with fewer corrections. Still, for critical systems, you should always add guardrails such as retry limits, timeouts, and fallback logic, because no LLM is infallible across arbitrarily long chains of tool calls.