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Small LLMs struggle with multi-step tool use in agentic applications

By PulseAugur Editorial · [1 sources] ·

A developer building an agentic assistant for battery engineering discovered limitations when using smaller language models, specifically the qwen2.5:7b model. While single-tool calls for simulations were reliable, multi-step tool orchestrations, such as comparing discharge rates, caused the model to drop steps or produce incomplete answers. This issue was exacerbated by the model's tendency to speculate on simulation results rather than strictly reporting them. The solution involved collapsing complex multi-tool plans into single, more comprehensive tools, thereby reducing the planning burden on the smaller language model. AI

IMPACT Highlights limitations of smaller LLMs in complex agentic tasks, suggesting a need for architectural changes or larger models for advanced orchestration.

RANK_REASON Developer's personal experience and findings on LLM limitations, not a formal release or research paper.

Read on dev.to — MCP tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Small LLMs struggle with multi-step tool use in agentic applications

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Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
Developer's personal experience and findings on LLM limitations, not a formal release or research paper.
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, model release
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AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
53 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [1]

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