# Choosing models and tools

> Not every task wants the biggest model or an agent. This topic covers matching a model to a task, weighing cost and speed against quality, choosing between a chat, an agent and an unattended automation, deciding which steps stay human, and re-applying that judgment as tools grow more capable.

Page: https://schubergphilis.github.io/ai-training/topics/using-agents/choosing-tools/
License: CC BY-SA 4.0, https://creativecommons.org/licenses/by-sa/4.0/

*Using agents* · topic `using-agents/choosing-tools`

Not every task wants the biggest model or an agent. This topic covers matching a model to a task, weighing cost and speed against quality, choosing between a chat, an agent and an unattended automation, deciding which steps stay human, and re-applying that judgment as tools grow more capable.

## Concepts

- **Model fit**: Matching a model to a task by what the task needs: a small fast model for classification or reformatting, a larger one for multi-step reasoning or ambiguous instructions, a model with tools where fresh facts matter. Fit is tested, not assumed: run a few representative inputs on each candidate and compare, rather than defaulting to the most capable and expensive option. [glossary](https://schubergphilis.github.io/ai-training/glossary/#model-fit)
- **Cost and speed**: The price and delay of a choice, per task and at the volume you expect. A model that costs ten times more and answers slower must be enough better to justify it; an agent that takes twenty tool calls costs twenty times a single answer. For repeated work, small differences per call become the whole budget, so measure before scaling. [glossary](https://schubergphilis.github.io/ai-training/glossary/#cost-and-speed)
- **Chat vs agent vs automation**: Three ways to apply a model. Chat: you steer every turn, best for thinking and one-off drafts. Agent: you brief it and it works through steps with tools, checking in as agreed, best for bounded tasks with clear done-criteria. Automation: it runs unattended on a trigger, best for repetitive, low-stakes, well-tested tasks. Autonomy rises across the three, so the checks must rise too. [glossary](https://schubergphilis.github.io/ai-training/glossary/#chat-vs-agent-vs-automation)
- **What to keep human**: The steps that stay with a person on purpose: decisions with legal or ethical weight, judgments about people, anything irreversible or costly, and the final accountability for a result. Keeping a step human is a design choice, and naming those steps early prevents them from being automated by default as tools improve. Keeping a human path also means the process keeps working when the tool is unavailable. [glossary](https://schubergphilis.github.io/ai-training/glossary/#what-to-keep-human)
- **Reasoning across tool levels**: Tools rise in abstraction, from autocomplete to chat to agents to systems of agents, and each level hides more of what happens below. The judgment about fit, cost, risk and what to keep human has to be re-applied at each new level rather than carried over. What was safe to trust at one level may not be at the next, and vice versa. [glossary](https://schubergphilis.github.io/ai-training/glossary/#reasoning-across-tool-levels)

## Links

- **Builds on:** [Delegating to an agent](https://schubergphilis.github.io/ai-training/topics/using-agents/delegating/), [Capabilities and limits](https://schubergphilis.github.io/ai-training/topics/concepts/limits/)
- **Leads to:** nothing yet
- **Competencies drawing on it:** [Chooses the right tool and autonomy level for a job](https://schubergphilis.github.io/ai-training/competencies/using-agents/chooses-tool-and-autonomy/)

## Lessons

- [Chat, agent or automation](https://schubergphilis.github.io/ai-training/using-agents/chat-agent-or-automation/) (explanation)
- [Reasoning across tool levels](https://schubergphilis.github.io/ai-training/using-agents/reasoning-across-tool-levels/) (explanation)
- [The steps that stay human](https://schubergphilis.github.io/ai-training/using-agents/steps-that-stay-human/) (explanation)
- [Trying two models on the same task](https://schubergphilis.github.io/ai-training/using-agents/trying-two-models/) (tutorial)

## Sources

- `AEC-12` [Getting started with Claude Code](https://lsimons.github.io/agent-engineer-course/12-getting-started-with-claude-code/), Agent Engineer Course (course)
- `Brilliant ABS` Reasoning across levels of abstraction, Brilliant, Coding with AI skills map (reference)
- `Academy ai-fluency-for-builders` [AI Fluency for builders](https://academy.claude.com/courses/ai-fluency-for-builders), Claude Academy (course)
- `Academy building-effective-human-agent-teams` [Building effective human-agent teams](https://academy.claude.com/courses/building-effective-human-agent-teams), Claude Academy (course)
- `OpenAI Academy agents-and-workflows-y0qoc` [Agents and Workflows](https://academy.openai.com/public/courses/agents-and-workflows-y0qoc), OpenAI Academy (course)
- `OpenAI Academy applied-ai-foundations-szsmv` [Applied AI Foundations](https://academy.openai.com/public/courses/applied-ai-foundations-szsmv), OpenAI Academy (course)