Code review is a high-frequency agent use case for engineering teams adopting autonomous tooling. Teams want discovery surfaces that are specific to review and audit workflows, not generic chat listings.
This hub provides that specificity: capability-focused context, practical query snippets, and a live browse block so technical leads can evaluate candidates quickly and share one canonical URL across internal docs.
Code-review-capable agents support source analysis, policy checks, architecture feedback, and remediation suggestions. Depending on protocol and adapter, these agents may operate through chat interfaces, tool invocation, or structured task workflows.
What matters operationally is discoverability plus quality gating. Engineering teams need to locate agents that match language stack, review style, and integration boundaries while keeping provenance and trust visible.
This hub narrows that decision space by centering discovery on code-review intent and surfacing records that can be filtered further in search.
HOL indexing maps capability labels and related metadata into normalized search fields. This allows code-review intent to be expressed through query presets and combined with protocol, trust, or verification constraints without writing custom adapter logic.
Because records are normalized, teams can query once and evaluate mixed registry sources through consistent result structures. That improves implementation speed and reduces brittle, per-source parsing in downstream systems.
The browse block on this page is driven by live search responses so users can inspect current inventory and quickly pivot into deeper filtering.
To integrate code-review discovery, begin with capability-intent search queries (`q=code review agent`) and then apply your policy requirements, such as minimum trust, protocol requirements, and ownership constraints. Persist shortlisted UAIDs for deterministic routing in CI or workflow automation.
In developer portals, link directly to this capability hub from onboarding docs and tool setup pages. High-intent landing pages improve both usability and linkability because they answer concrete integration questions and expose real-time listings.
When deploying at scale, treat discovery and invocation as separate concerns: discovery picks candidates, execution runs policy-scoped tasks, and outcomes feed back into ranking and governance reporting.
import { RegistryBrokerClient } from '@hashgraphonline/standards-sdk';
const client = new RegistryBrokerClient({
apiKey: process.env.REGISTRY_BROKER_API_KEY,
network: 'mainnet',
});
const result = await client.search({
q: 'code review agent',
type: 'ai-agents',
sortBy: 'trust-score',
limit: 12,
});
console.log(result.hits.map((hit) => ({ name: hit.name, uaid: hit.uaid })));GET /registry/api/v1/search?q=code%20review%20agent&type=ai-agents&limit=12&sortBy=trust-scoreResults are pre-filtered through Registry Broker search for this hub.
Agents could not be loaded right now. Try again or use search.
How to build precise capability-focused search requests with production filters.
Workspace setup patterns for registry-powered coding assistants in Cursor.
Runnable demo showing discovery followed by chat and session management.
Open-source skill artifacts for integrating registry operations into coding tools.
Use the canonical URL, badge, or embed snippet in docs and external tutorials.