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What are Review Agents?

Build review agents for your product. Point an agent at a URL, and it reviews the content and posts findings as comment annotations. Review agents can review anything you can surface:
  • Web pages and sites: text, screenshots, HTML, CSS, links, accessibility tree, Lighthouse.
  • Your data: pull any REST endpoint into the review with the rest-api strategy.
  • Charts, dashboards, and more: anything you can expose via an API or MCP server.
You write a prompt and pick a few config options. The platform handles context gathering, LLM invocation, post-processing, deduplication, and annotation creation. You can also put agent creation in your users’ hands: ship the built-in agents as defaults, and let users create custom agents in plain English from your UI. The prompt tools turn a one-line instruction into a production agent config.

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What you get

  • Built-in and custom agents. Ready-made agents (spell-check, broken-links, PII, Lighthouse, accessibility, and more), or your own from a prompt.
  • Plain-English agent builder. Let your users describe a review in simple English; prompt tools (enhance, validate, refine) check it, expand it into a full agent config, and iterate on feedback. userContextFields render typed inputs in your setup UI.
  • Pluggable context gathering. Stack strategies: page text, screenshots, HTML, CSS, links, accessibility tree, computed styles, robots.txt, sitemap, Lighthouse, live REST API data.
  • Live REST API context. The rest-api strategy fetches your endpoints at execution time and injects the responses into the prompt. Secrets are encrypted at rest and redacted on read.
  • MCP tool use. The mcp-tools strategy runs a multi-turn tool loop against your remote MCP servers. Works with Claude and Gemini.
  • Versioned configurations. Every behavioral edit creates a new version; one-click restore rolls back.
  • Async execution. Run Execution returns an executionId immediately; poll Get Execution until status !== "running".
  • Cross-page execution. Set crossPageExecute: true to crawl the seed URL and review up to maxUrlsToProcess pages.
  • Device emulation. Run each execution as "mobile" or "desktop".
  • Re-run deduplication. Re-running an agent replaces the prior run’s suggestions instead of piling up duplicates.
  • Token usage analytics. Per-agent, per-model, per-month token usage via the Analytics endpoint.
  • Agent groups. Bundle related agents (e.g. “Brand QA”) and filter lists by group.

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Use cases

Brand consistency

Verify brand colors, typography, and logo placement across your marketing pages. Re-run on every deploy.

Pre-launch QA

Run spell-check, broken-links, and accessibility agents before a release. Findings land on the staging document.

Content moderation

Flag PII and profanity on user-generated pages. Pair with Approval Engine for human review.

Cross-page audit

Set crossPageExecute: true and the crawler discovers internal links, then reviews each page.

Data-enriched checks

Pull live business data into the prompt with rest-api, e.g. validate on-page pricing against your billing API.

Workflow integration

Trigger an agent from an Approval Engine workflow node and park the workflow on its findings.

User-built agents

Let your users create review agents in plain English from your UI. Ship built-in agents as defaults; prompt tools turn user instructions into custom ones.

Docs accuracy

Use mcp-tools to verify on-page code snippets live against your documentation MCP server.

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How it works

  1. Define the agent. Call Create Agent with a name, instructions, and config (context gathering, execution strategy, post-processing).
  2. Run an execution. Call Run Execution with agentId and url. Returns an executionId immediately.
  3. Poll for results. Call Get Execution until status !== "running". Set includeResults: true for per-URL findings.
  4. Findings appear as annotations. Each finding becomes a comment annotation on the document referenced by organizationId / documentId (on by default).

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Mental model

Execution lifecycle:

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Pipeline phases

Each agent run executes sequential phases:

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Execution statuses

Terminal status precedence: error > partial > failed > passed. A run that produced findings but had a single failing page surfaces as partial (not error). For partial/error, per-page failure detail is in resultsSummary.urlsErrored and resultsSummary.erroredUrls.

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Built-in agents

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Context gathering strategies

Stack one or more strategies in the order you want them to run. Per-strategy overrides are supplied under contextGathering.strategyOptions["<strategy>"]. See Create Agent for the rest-api endpoint configuration schema.

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Execution strategies

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Knowledge (Memory-RAG)

Agents can pull workspace knowledge from Velt Memory into the prompt via execution.knowledge:
The knowledge block is strictly validated: only the four fields above are accepted. The legacy sourceIds field has been removed, and maxChunks is now capped at 20 (previously 50). Knowledge retrieval never fails an execution: if Memory is unavailable, the run continues in a degraded mode without knowledge context.

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Scope

organizationId and documentId are required on Run Execution and control where annotations land. The document must already exist before you run an agent against it.

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Get started

Setup

Create an agent, run it against a URL, and read the findings end-to-end.

API Reference

All endpoints organized into Agents, Execution, Versioning, Prompt Tools, Analytics, and Groups.