Edgescan Atomic (agentic autonomous penetration testing in the Edgescan platform) vs Maze AI Agents (Maze Cloud and Maze Code)
Edgescan Atomic (agentic autonomous penetration testing in the Edgescan platform) (Edgescan (BCC Risk Advisory Limited)) and Maze AI Agents (Maze Cloud and Maze Code) (Maze) are both listed in the Security category of Agent Index. They share no framework tags in our dataset.
| Field |  Edgescan Atomic (agentic autonomous penetration testing in the Edgescan platform) |  Maze AI Agents (Maze Cloud and Maze Code) |
|---|---|---|
| Vendor | Edgescan (BCC Risk Advisory Limited) | Maze |
| HQ | Ireland · Europe | United Kingdom · Europe |
| Primary category | Security | Security |
| Also listed in | Security | Security, Code security |
| Summary | Autonomous penetration testing built into the Dublin-based Edgescan platform, launched October 7, 2026. A team of narrow AI agents plans, adapts, chains vulnerabilities and validates real impact on web applications, using Edgescan's existing asset context and findings, while a deterministic 'Harness' enforces scope, permissions, approvals and allowed actions. Findings land alongside Edgescan's DAST, PTaaS and ASM results. | UK AI agents that investigate every cloud and code vulnerability with code-to-cloud context, decide exploitability and deliver fixes. |
| Framework tags (Agent Index) | — | — |
| Frameworks (vendor-claimed) | — | — |
| Capabilities / tasks | pen test management, attack surface, vulnerability management, appsec, exposure management | cloud security, vulnerability management, remediation, sca, sast |
| Pricing (as published) | Not published. Atomic runs on Atomic Attack Credits assigned per asset in the Edgescan platform; pricing by quote/demo. | Not published. |
| Pricing model | quote | quote |
| Primary source | https://www.edgescan.com/introducing-edgescan-atomic/ | https://mazehq.com/platform/cloud |
| Last checked | 2026-10-19 | 2026-10-18 |
| Official site | https://www.edgescan.com/the-platform/atomic-autonomous-penetration-testing-edgescan/ | https://mazehq.com/platform/cloud |
Side-by-side data is generated from the open dataset. “Vendor-claimed” frameworks are as marketed by each vendor and not verified by Agent Index; framework tags indicate relevance, not certification. Request current quotes from each vendor before buying. This page does not rank or score vendors. Sponsored comparisons, if any, will be labelled.
Also in this use case
Other verified listings that share a task tag or use-case group with Edgescan Atomic (agentic autonomous penetration testing in the Edgescan platform) or Maze AI Agents (Maze Cloud and Maze Code). Open a pair page or the use-case table — this is not a ranking.
- AISLE (autonomous vulnerability discovery, triage and verified-fix agents) — Prague-headquartered AI code-security platform in which thousands of agents analyse a codebase in parallel for exploitable vulnerabilities, fix agents write patches, and agentic verification builds the repo and runs tests before opening a pull request for human merge. October 2026 added agent-driven proof of exploitability against running web apps and APIs. Cohere signed a letter of intent to acquire AISLE on Oct 15, 2026.
- Bricklayer AI security agents (agentic platform for the AI security workforce) — Agentic cybersecurity platform that deploys, coordinates and governs teams of role-based and tool-specialist AI agents to triage alerts, investigate incidents, run threat-intelligence and threat-hunting work and track vulnerability remediation inside analyst-approved procedures, with every agent action auditable. · Bricklayer AI security agents (agentic platform for the AI security workforce) pair comparison
- Check Point Agentic Exposure Validation (AEV, AI agents that prove external exploitability) — Part of Check Point Exposure Management, launched May 28, 2026. AI agents reason like attackers across an organisation's external attack surface, correlating exposures, leaked credentials, CVE and live exploit research with existing protection coverage, then safely prove, pivot on, or discard each finding, so teams get evidence of what is actually exploitable. · Check Point Agentic Exposure Validation (AEV, AI agents that prove external exploitability) pair comparison
- Cloudgeni cloud agents (Compliance Remediation, Drift & Import, PR Review and other infrastructure agents) — Oslo-built agentic cloud operations platform whose agents find cloud security and compliance issues, investigate across cloud state and IaC, write the Terraform/Bicep fix, validate it with a plan and policy checks and open a pull request, then verify the fix after it lands. The Compliance Remediation Agent works from Prowler, Security Hub, Defender and Checkov findings against CIS, SOC 2, ISO 27001 and NIS2. Autonomy is set by policy. · Cloudgeni cloud agents (Compliance Remediation, Drift & Import, PR Review and other infrastructure agents) pair comparison
- depthfirst autonomous security agents (code, supply chain, secrets and agentic pentesting) — San Francisco application-security platform whose autonomous AI agents trace business logic and data flows to find exploitable code vulnerabilities, check dependency reachability and secrets, review pull requests with proposed fixes, and run agentic pentests against running applications, re-running each attack after a fix merges to confirm it is closed.
- Nullify autonomous product-security agents (find-to-fix with merge-ready PRs and AI Pentest Agent) — Australian 'autonomous system of agents' for product security that covers code, secrets, API pentesting and exploit validation. It proves findings with reproducible evidence, scores them against the customer's assets, writes merge-ready fix PRs routed to code owners, and runs remediation campaigns through Jira and Slack until merge, with the customer keeping final merge approval.
- Ridge Security RidgeGen (agentic AI red teaming) and RidgeBot — Offensive-security vendor from Milpitas, California. Its on-premises, model-agnostic RidgeGen agent does agentic AI red teaming: it reasons through multi-step attack chains to find business-logic and non-CVE risks, verifies every finding with reproducible exploit evidence, gives fix guidance and re-runs to confirm fixes. It sits alongside RidgeBot automated pentesting. · Ridge Security RidgeGen (agentic AI red teaming) and RidgeBot pair comparison
Use-case comparisons
- Agentic pentesting AI agents — 19 listings (Autonomous or AI-assisted penetration testing, attack-surface testing and exploitable-path verification)
- Cloud security (CNAPP) AI agents — 8 listings (Cloud and container security posture, exposure and remediation)
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