https://www.iaifeed.com iAIFeed Wed, 30 Sep 2026 12:46:03 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.2 https://www.iaifeed.com/wp-content/uploads/2026/07/cropped-iaifeed1-80x80.png https://www.iaifeed.com 32 32 https://www.iaifeed.com/how-to-reduce-glm-5-token-costs-in-long-agent-runs https://www.iaifeed.com/how-to-reduce-glm-5-token-costs-in-long-agent-runs#respond Wed, 30 Sep 2026 12:46:03 +0000 https://www.iaifeed.com/how-to-reduce-glm-5-token-costs-in-long-agent-runs To cut GLM-5 token costs in long agent runs, summarize completed steps, drop raw tool output after use, and cap the loop length. A 40-step agent re-sends its whole history on every step, so cost grows faster than the task does. These changes often cut the bill in half.

Why Do Agent Runs Get Expensive Fast?

Because the API is stateless. Every step sends the full conversation back. Step 1 sends 1 unit of history, step 40 sends 40. The total is the sum, which is quadratic in step count. A task that looks like “one job” is actually hundreds of billed calls, and the history you carry is most of the payload.

What Are the Five Levers?

1. Summarize done steps. Replace a completed step’s raw output with a one-line result before the next call.
2. Drop used tool output. Once a search result is consumed, it does not need to stay in context.
3. Cap the loop. Set a hard maximum step count so a stuck agent stops instead of spinning.
4. Trim tool schemas. Send only the tools relevant to the current phase, not the full catalog.
5. Batch read-only calls. Fetch several facts in one step instead of one call each.

The first two do most of the work. History is the biggest part of every request, and it is the part you control.

How Much Can You Actually Save?

Lever	Typical effect
Summarize steps	Large cut to history size
Drop used output	Moderate
Cap the loop	Stops worst cases
Trim schemas	Small per-call
Batch reads	Fewer steps overall

The numbers depend on your step count and output sizes, so measure your own runs. But the direction is reliable: shorter history means a smaller bill on every remaining step.

What Breaks If You Over-Trim?

The agent forgets why it took an action and repeats work. Summaries must keep the decision and its result, just not the raw payload. If you strip too hard, the model loses the thread and loops on a problem it already solved – which costs more than the tokens you saved. Keep the “what happened and what it means” line, drop the “here is the full response body” blob.

Does This Apply to Other Models?

Yes. Any stateless agent loop has this shape, whether on GLM-5 or a coding model like Grok Code Fast. The levers are model-agnostic. What changes is how terse the model can be in summaries before it loses accuracy.

Quick Summary

• Agent cost grows quadratically because history is re-sent every step.
• Summarize completed steps and drop used tool output first.
• Cap the loop so a stuck agent cannot spin forever.
• Over-trimming causes repeated work – keep decisions, drop payloads.
• The same levers work on any stateless agent loop.

]]> https://www.iaifeed.com/how-to-reduce-glm-5-token-costs-in-long-agent-runs/feed 0 https://www.iaifeed.com/how-to-use-glm-5-for-agentic-coding-tasks https://www.iaifeed.com/how-to-use-glm-5-for-agentic-coding-tasks#respond Wed, 30 Sep 2026 12:45:41 +0000 https://www.iaifeed.com/how-to-use-glm-5-for-agentic-coding-tasks GLM-5 is Zhipu AI’s flagship language model, positioned for agentic work – tool calling, multi-step planning, and long tasks. You call it through Zhipu’s API with an OpenAI-style request. Wiring it into an agent loop takes an afternoon; tuning tool schemas takes longer.

What Does GLM-5 Do Better Than a Chat Model?

Tool calling. It is built to decide when to call a function, format the arguments, and continue after seeing the result. That loop is what makes an agent, and a model that fumbles it makes a useless agent. GLM-5 is solid at keeping the loop going across many steps without losing the goal.

How Do You Set Up an Agent Loop?

1. Define your tools as JSON schemas with clear names and required fields.
2. Send the user goal plus the tool list in the first request.
3. If the model returns a tool call, run it and append the result as a message.
4. Repeat until the model returns a final text answer.
5. Cap the loop at a fixed number of steps to stop runaway agents.

The step cap is not optional. An agent that loops on a failing tool will burn tokens indefinitely if you let it.

What Are the Two Limits That Bite First?

Tool schema clarity and context growth. Vague tool descriptions cause the model to pick the wrong function, and every loop iteration adds to the context until the history itself becomes the problem. Fix the first by writing descriptions as if for a new teammate. Fix the second by summarizing old steps and dropping raw tool output once it has been used.

How Much Does GLM-5 Cost?

Use	Note
Standard tier	Per-token, competitive with US models
Agent loops	Cost scales with steps, not tasks
Free credits	Usually available on signup

Agent work multiplies token cost because one task is many calls. Test with a step cap and short goals before you point it at anything long. If your work is a phone-side agent rather than code, that is a different product – see AutoGLM.

Which Tasks Fit GLM-5 Well?

Operations that follow a repeatable tool sequence: fetching data, transforming it, writing results, checking. Also good for research loops that search and summarize. Less good for open-ended creative planning where the goal is fuzzy – the agent needs a crisp success condition to know when to stop.

Quick Summary

• GLM-5 is Zhipu AI’s flagship model built for tool calling and agent loops.
• Define clear tool schemas and cap the loop at a fixed step count.
• Tool schema vagueness and context growth are the first failures.
• Agent cost scales with steps, not tasks – measure it.
• Best for repeatable tool sequences, not fuzzy creative goals.

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