> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ironbee.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Recommendations

> Concrete directives generated from findings, injected into the AI agent's context automatically.

**Recommendations** are actionable directives generated from findings. Every recommendation is tied to a specific finding (`concern` or `critical` severity) and grounded in observed session data.

## What makes recommendations different

Recommendations are not generic advice, they are imperative instructions written specifically for the AI coding agent. They are automatically injected into the agent's system prompt on the next session, so the agent adjusts its behavior without you needing to manually relay the feedback.

Example recommendations:

* *"Always check contrast ratio, form validation, and error states before writing a verdict."* (quality finding)
* *"After a fix, re-run all checks, not just the failing one."* (efficiency finding)
* *"Button.tsx has a 70% fail rate, take extra care when modifying this file."* (patterns finding)
* *"Prefer prompts with stable cached prefixes when working in src/auth/ and current cache hit rate is below 30%."* (cost finding)

## Recommendation scope

| Scope          | Injected when                               |
| -------------- | ------------------------------------------- |
| Project-scoped | Agent works in any session for that project |
| Account-scoped | Agent works in any project for that account |

Project-scoped recommendations come from Quality, Cost, and Session Insights analysis. Account-scoped recommendations come from Account Cost and Account Session Insights analysis.

## Recommendations list

The Recommendations tab shows all active recommendations, linked to their source findings. Each recommendation includes:

* **Action:** what the agent should do, written as a clear directive
* **Source finding:** the observation that generated it (title, area, severity)
* **Status:** active or dismissed

## Dismissing recommendations

You can dismiss a recommendation if it is not relevant to your current work. Dismissed recommendations are not injected into the agent and are preserved separately from active recommendations.

When a new analysis run supersedes the current one, previously-dismissed recommendations are preserved, they are not re-activated.

## Feedback

You can mark a recommendation as helpful or not helpful. This feedback helps improve analysis quality over time for your project.