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14/09/2026 10:10 am
TL;DR: As agents increasingly write, review, and debug code, Sonar argues that progressive disclosure and high-signal PR context become the difference between efficient agent work and costly full-repo exploration. The practical implication is that code review, commit history, and linked workflow data now function as governance controls for AI-assisted engineering rather than documentation hygiene.
NHIMG editorial — based on content published by Sonar: progressive disclosure and agent-ready codebase context
Questions worth separating out
Q: How should security teams implement governed context for AI agents in enterprise environments?
A: Security teams should place a governance layer between agents and source systems so the agent receives trusted meaning, policy, and lineage with the data.
Q: Why do structured PR descriptions matter for AI-assisted engineering?
A: Structured PR descriptions reduce unnecessary exploration, improve decision quality, and create a reusable record of intent that agents can follow later.
Q: What are the signs that an AI agent has too much workflow context?
A: Common signs include agents repeatedly searching across unrelated files, pulling in tickets that are not relevant to the change, citing vague summaries, or making edits that satisfy code mechanics but miss business intent.
Practitioner guidance
- Standardise PR summaries for agent consumption Require every agent-facing pull request to include a concise change summary, why it exists, breaking changes, and related tickets so the agent does not need to infer intent from the diff alone.
- Treat review rationale as reusable governance evidence Capture performance, security, and maintainability decisions in review comments that can be searched and reused by future agents instead of relying on short approval phrases.
- Define cross-system context boundaries Document which ticketing, CI, telemetry, and incident systems an agent may query for each workflow, and log every context lookup for auditability.
What's in the full article
Sonar's full article covers the operational detail this post intentionally leaves for the source:
- Examples of agent-ready PR summaries, commit messages, and review notes that teams can adopt directly.
- The "eyes, hands, and legs" operating model for connecting repository context to adjacent workflow systems.
- Concrete workflow patterns for linking issues, CI, and code changes without losing the original explanation.
- Practical examples of how to preserve review history so future agents can reuse team decisions.
👉 Read Sonar's analysis of progressive disclosure for agent-ready codebases →
Agent-ready codebases: is your context structured enough for AI reviewers?
Explore further
14/09/2026 10:10 am
Progressive disclosure is emerging as a governance pattern for agentic engineering, not just a usability pattern. The article correctly shifts attention from code generation to context selection, because agents fail most often when they are forced to infer intent from incomplete or noisy signals. In identity terms, that means the runtime must know not only what the agent may do, but what surrounding information it may consume. Practitioners should treat context scoping as part of the access model, not an afterthought.
A question worth separating out:
Q: What is the difference between code access and context access for AI agents?
A: Code access determines what the agent can modify. Context access determines what surrounding information the agent can read to make decisions. Those are not the same control, because a narrowly scoped agent may still overconsume tickets, logs, or review history if context is not separately governed. Practitioners need both controls.
👉 Read our full editorial: Progressive disclosure for agent-ready codebases needs structured context
