AI coding and agents

Agent Identity

Also known as Agent Identity concept

By WeavePublished 1 min read

Definition

Agent Identity is a software-engineering concept describing how an AI coding system, its tools, or human collaborators handle a defined task.

How agent identity works

Agent Identity is a focused part of an AI-assisted software workflow. It is useful when inputs, permitted actions, boundaries, and acceptance evidence are explicit. The design should make clear what the model decides, what tools execute, and where an engineer remains responsible.

A concrete example

A team asks an agent to handle agent identity while updating an authentication library across a monorepo. The agent searches call sites, proposes a scoped change, runs focused tests, and leaves a reviewable diff. Engineers can inspect the evidence and challenge an unsupported assumption before the work is merged.

Operational considerations

Record repository state, tool results, generated edits, checks, review outcome, and final task status. Compare representative tasks rather than one impressive demonstration. Keep access control, ownership, and rollback rules separate from the model's ability to produce text or code.

Limitations

This concept does not guarantee correct code. Missing context, repository conventions, tool failures, ambiguous requirements, and evaluation gaps can change the result. Activity volume is not completed value, and consequential changes still require appropriate human review.

How this relates to Weave

Weave's Token Intelligence can help connect agent identity with AI usage, spend, engineering work, review effort, and outcomes. Use that context to study the workflow, but do not infer capability, authorship, or success from token volume alone.

Explore Token intelligence

Sources and further reading

  1. OWASP Top 10 for LLM Applications