About Agent Model Fit

Agent Model Fit is a research and implementation publication for digital and IT leaders deciding where AI belongs in real work. We also write for business owners and curious practitioners who want enough technical detail to make a sound decision without needing a computer-science background.

Right agent. Right model. Right problem. Start with the outcome people need, the evidence available, and the consequences of getting it wrong. Then decide which work needs a model, which needs an agent with tools, and which should remain a conventional process or a human decision.

Capability with accountability

Our operating view is that agents should help people produce more useful work and expand what an organization can do. That includes the work of defining a problem, checking an output, handling an exception, and deciding when to stop. Human judgment, accountability, and approval boundaries belong in the design.

We are vendor-neutral. We examine model choices in relation to the task, available evidence, cost, risk, and oversight required. We do not assume that the strongest or most expensive model is the right choice for every step.

What we cover

• Framework: ways to reason about agent, model, and workflow decisions.
• Fieldwork: documented implementation choices, corrections, and lessons.
• Guides: practical procedures a reader can adapt to a defined task.
• Evaluations: how to assess outputs and interpret comparison evidence.
• Business: organizational value, readiness, operating costs, and tradeoffs.
• Case Studies: bounded accounts of a problem, intervention, and outcome, where the evidence supports them.

These are our editorial areas, not a claim that each already contains a body of published results. Explore the sections as the publication grows.

What counts as evidence

We distinguish observed results from reported history, recommendations, illustrations, and open questions. A useful framework need not be an experiment, but it must not borrow the authority of one. Where we report original work, we explain the conditions, evidence, limitations, and human intervention that matter to the result.

We disclose material agent contributions, identify sources beside the claims they support, and describe substantive corrections. We do not turn missing measurements into savings estimates or a successful attempt into a reliability claim. Human editorial review and explicit approval are required before release.

The publication is part of the work

AMF itself uses agents within a Git-native workflow: versioned source files, bounded assignments, reviewable changes, validation records, and human approval. That gives us concrete decisions to examine, including where instructions or handoffs need correction. It does not establish a measured productivity gain or prove that the process prevents errors.

The site uses a lightweight static foundation. We aim to make useful original information readable by people and retrievable by agents through semantic pages, stable addresses, explicit dates, and clear provenance. The goal is material a reader can inspect, question, and use.