Yes, GLM-5 can handle multi-turn agent workflows robustly if you design the workflow to be tool-driven, stateful, and checkpointed rather than relying on raw chat history. GLM-5 is explicitly positioned around “agentic engineering” capabilities in the official docs and launch materials, and it supports a large context window (200K) that helps with longer tasks. But robustness doesn’t come from context size alone; it comes from controlling how the agent plans, calls tools, and persists state. In production, you want GLM-5 to behave like a system component that follows rules: it should ask for missing inputs, call tools when needed, and stop when done. Start with the official GLM-5 overview for agent positioning: GLM-5 overview and the launch post GLM-5 blog.
A robust multi-turn agent design usually has these building blocks:
State object (machine-readable)
Keep a structured “agent state” (JSON) that includes: user goal, constraints, open TODOs, artifacts created, and last tool results. Don’t depend on the model to remember everything from earlier turns; re-inject the state each turn.Plan → Act → Verify loop
Force a loop discipline. For example:Turn A: produce a plan + required tools
Turn B: execute tool calls (retrieval, code search, tests)
Turn C: synthesize answer + next steps
If you let the model free-run, it will sometimes skip verification or invent outcomes.
Tool calling with validation
Define tools with strict JSON schemas and validate arguments before execution. Z.ai documents tool/function calling for GLM models: Function Calling. Even if you don’t expose risky tools, validate to prevent nonsense parameters and infinite loops.Context management and compaction
Even with 200K context, you don’t want to append endless transcripts. Summarize and compact older steps into the state object. (Z.ai’s migration notes highlight the large context, but good agents still manage context actively): Migrate to GLM-5.
A simple reliability trick is to require the agent to output a “checkpoint” after each major step: what changed, what was verified, and what remains.
Multi-turn agents almost always need retrieval. The agent should not “remember” your docs—it should fetch them. Implement a search_docs tool that queries a vector database such as Milvus or Zilliz Cloud with metadata filters (version, lang, product). Then instruct GLM-5: “If the user asks about product behavior, call search_docs and cite chunk IDs.” This makes the agent robust across turns because the grounding step repeats consistently. It also makes debugging straightforward: when a user says “that’s wrong,” you can inspect retrieval results and fix chunking/filters rather than guessing what the model “thought.”