How does Redline AI compare with Akto for testing and protecting production AI agents?

Redline AI and Akto, side by side

Redline facts in this comparison come from Redline’s own product documentation. Akto facts come from Akto’s product pages and documentation, linked in the Sources section. Where a cell does not describe how Akto performs an activity, Akto’s published pages do not state how it does so.

The central distinction is the boundary at which each product operates. Redline connects to an agent that the engineering team already owns and runs. Akto’s Argus product begins by finding assets across the organisation and then provides testing and runtime controls for those assets.

The products also describe their testing inventories differently. Redline states a case count and attack-family count. Akto describes probe templates, with different counts appearing on its documentation and agentic security product page. These figures indicate different testing models and should not be treated as a direct like-for-like benchmark.

How do the products test agents before release?

Akto’s red teaming is probe-driven. Its documentation describes 4,000+ AI-specific probe templates covering the OWASP Top 10 for LLMs. The named areas include prompt injection, jailbreaking, system-prompt extraction, excessive agency and unauthorised tool use. The tests can run against an agent imported by endpoint URL, with CI/CD integration for continuous scanning.

Akto’s broader product material uses a different figure on its agentic security page: 1,000+ probes. The comparison should therefore preserve the distinction between the documentation and the product page rather than combine the values. Akto also describes testing of agents, MCP servers and AI models, together with discovery of assets across cloud environments.

Redline tests the assembled agent where it runs. Its SDK connects outbound, so there is no endpoint to import and nothing new is exposed. Each run receives its own container, with the agent’s real tools, MCP servers and skills attached. This matters when the behaviour under test depends on the actual tool set or MCP configuration rather than an abstract model interaction.

Redline’s corpus contains 11,204 cases across 16 attack families, mapped to OWASP Agentic Security Initiative classes. A task passes only when the agent completes the intended job and refuses the attack. A second agent decides the result by inspecting the machine and its record after the run, rather than relying only on the conversation transcript.

For engineering teams, the practical choice is between breadth of asset discovery and depth of execution against a connected agent:

• Akto begins by locating agents, MCP servers and GenAI applications, then applies probe templates to imported or discovered assets.
• Redline begins with the agent selected by the team, attaches its real execution environment, and evaluates the resulting machine evidence.
• Akto documents CI/CD scanning for continuous testing.
• Redline provides a pre-release test path for agents that can connect outbound through its SDK.

See how to test an AI agent before you ship it for the Redline testing model.

Where do runtime controls run?

Akto documents runtime guardrails as policies that restrict the actions, tools, topics and data an agent can reach. Traffic is routed through the MCP Gateway or AI Gateway, providing the control point described in Akto’s documentation.

This architecture starts outside the agent process. It is suited to a team that wants central policies applied across discovered or connected assets, including MCP servers and AI applications. Akto’s published material names runtime policies and gateway enforcement, but does not describe the internal evaluation stages of the agent process in the same terms as Redline.

Redline’s runtime controls run inside the agent process. They evaluate activity at four stages:

• When a message arrives, at pre_input.
• Before a tool call, at pre_tool.
• After a tool returns, at post_tool.
• Before the agent sends its reply, at pre_output.

A rule can see the tool name and every argument. Its verdict can be deny, require approval, warn or log. This gives the policy decision access to the proposed operation before the tool call, and to the returned result after the tool has run.

Redline also treats prompt-injection detection as a separate gate at the input boundary. Redline Guard v5 is published with a benchmark of 0.9484 recall at 1.4% false positives on BIPIA, including the three sets it loses. The published benchmark is therefore part of the product’s stated evidence for this control rather than an undisclosed internal measure.

The distinction is not simply whether both products have guardrails. It is where the decision is made and what context the decision can inspect. Akto documents gateway-based control over traffic and policies. Redline documents in-process evaluation over messages, tool names, arguments, tool results and output.

Read more about runtime guardrails for AI agents and how accurate prompt-injection classifiers are.

What production coverage does each product provide?

Akto’s production coverage is broad across an organisation. Its documentation describes an inventory of agents and MCP servers in development, staging and production. It also describes alerts for shadow and zombie agents, alongside behavioural detection of agent loops, unauthorised tool calls and excessive usage.

Akto Atlas extends that scope to employee AI tools. The published description includes an endpoint shield, browser extensions, and hooks for Cursor, Claude Code, Codex and other command-line tools. This provides a documented path for visibility into AI tools employees run on their own machines.

Redline’s production coverage is focused on the agents connected to it. One decorator streams every production conversation as a session. Rules match every span as it is written and raise violations. Closed sessions are mined for recurring user intents, and honeypot tools turn a hijack into a recorded call with the session attached.

This creates continuity between testing and production enforcement. The agent tested before release is the agent observed during live operation, and the same style of rules can be applied while sessions are written. The production view is deep on connected agents, but it does not discover agents that nobody has connected to Redline.

Redline also does not cover employee AI tools. That is an explicit boundary in the comparison, not an implied capability. Teams evaluating the products should therefore separate two questions:

• Which agents and AI tools exist across the organisation?
• Which owned agents need execution-level testing and in-process production controls?

Akto addresses the first question through discovery, inventory and Atlas. Redline addresses the second for agents connected through its SDK.

See AI agent honeypots for the production detection model described by Redline.

Which product fits an engineering or security team?

The selection depends on the first operational problem the team needs to solve.

Choose Akto when discovery comes first

Akto is the closer fit when security does not yet have a complete inventory of agents, MCP servers and GenAI applications. Its published material describes discovery across cloud environments through 50+ connectors and eBPF, followed by posture information, red teaming and runtime policies.

This approach also fits organisations that need coverage across development, staging and production, or that need visibility into employee AI tools. Akto’s documented capabilities include shadow and zombie agent alerts, behavioural detection and Atlas coverage for tools on employee machines.

The relevant Akto workflow is:

• Discover agents, MCP servers and GenAI applications.
• Import or identify assets for testing.
• Apply AI-specific probe templates, including through CI/CD.
• Route traffic through the MCP Gateway or AI Gateway.
• Monitor for the documented behavioural conditions and unmanaged assets.

Choose Redline when owned agents come first

Redline is the closer fit when the team already knows which agents it builds and wants to test their actual assembled execution environment. The SDK connects outbound from where the agent runs, while each test run gets its own container with real tools, MCP servers and skills attached.

Redline also fits teams that want the test result tied to evidence from the machine. The second agent inspects the run’s machine and record, and a criterion passes only on evidence it saw. In production, the same connected agents can be controlled inside the process and observed through live sessions, span-level rules, recurring intent analysis and honeypot tools.

The relevant Redline workflow is:

• Connect the agent with the Redline SDK.
• Run 11,204 cases across 16 attack families.
• Review results based on machine evidence.
• Enforce rules at message, tool-call, tool-result and output stages.
• Stream and evaluate production sessions for violations and recurring intents.

Neither choice removes the need to define the assets and behaviours that matter to the organisation. The difference is where the products begin: Akto begins with organisational discovery, while Redline begins with the agent the team has selected and connected.

What should a buyer compare during evaluation?

A useful evaluation should preserve the distinction between inventory coverage, test execution and runtime enforcement. Counting probes or cases alone will not establish whether the products test the same object or use the same result criteria.

For pre-release testing, ask what reaches the agent and what the test runs against. Redline states that its SDK connects outbound and tests the assembled agent with its real attachments. Akto documents endpoint import, discovered assets, agents, MCP servers and AI models, together with CI/CD scanning.

For result quality, ask how a pass or failure is determined. Redline describes a second agent inspecting the machine and record. Akto describes scan results with severity levels and remediation steps. These are different result models.

For runtime protection, ask where policy evaluation occurs and what information a rule can inspect. Akto describes gateway routing and policies over actions, tools, topics and data. Redline describes in-process stages, tool names, arguments and verdicts including approval, denial, warning and logging.

For production coverage, ask whether the objective is organisational discovery or deep coverage of connected agents. Akto documents discovery, shadow and zombie agent alerts, behavioural detection and employee-tool coverage through Atlas. Redline documents live sessions, span-level rules, recurring user intents and honeypot tools for connected agents.

Key takeaways

• Akto starts with discovery across cloud environments, while Redline starts with the agent the engineering team has built and connected.
• Redline tests 11,204 cases across 16 attack families and evaluates results using evidence from the run’s machine and record.
• Akto documents 4,000+ AI-specific probe templates, while its agentic security product page says 1,000+; the counts describe different published materials.
• Akto applies runtime controls through its MCP Gateway or AI Gateway. Redline evaluates policies inside the agent process at four stages.
• Akto documents organisational and employee-tool discovery, including shadow and zombie agent alerts and Akto Atlas.
• Redline focuses on connected agents, with production sessions, span-level rules, recurring user intents and honeypot tools.

Common questions

Is Redline AI an alternative to Akto?

For agents a team builds, yes. Both products red-team agents before release and provide runtime controls. They address different starting points, however. Redline covers agents connected to it through the SDK. Akto documents discovery of agents that security teams may not know about, together with coverage for employee AI tools on laptops through Akto Atlas.

How many attacks does each run?

Redline runs 11,204 cases across 16 attack families. Akto’s documentation describes 4,000+ AI-specific probe templates, while its agentic security product page says 1,000+. These figures are not directly comparable because a probe template and a test case are counted differently.

Does either need the agent exposed as an endpoint?

Akto’s agent import takes the agent’s endpoint URL. Redline does not use an endpoint for this connection. Its SDK connects outbound from wherever the agent runs and pulls work, so the agent can run from a laptop behind NAT without exposing a new endpoint.

Where do runtime controls run?

Akto routes traffic through its MCP Gateway or AI Gateway for central control. Redline evaluates policies inside the agent process. At the tool-call stage, a Redline rule can see the tool name and every argument before the operation happens, and it can return a deny, require approval, warn or log verdict.

Does Redline discover agents that are not connected to it?

No. Redline’s production coverage applies to the agents connected through its SDK. The comparison states that Redline does not discover agents nobody has connected. Akto’s published material describes discovery of agents, MCP servers and GenAI applications across cloud environments, together with shadow and zombie agent alerts.

Does Redline cover employee AI tools?

No. Redline’s comparison row marks employee AI tools as not covered. Akto documents Akto Atlas for this area, including an endpoint shield, browser extensions, and hooks for Cursor, Claude Code, Codex and other command-line tools.

Sources

• Akto, Agentic Security product page — 80+ connectors, 1,000+ probes, discovery, red teaming, guardrails and runtime protection.
• Akto, AI Security page — 4000+ probes; detection of agent loops, unauthorised tool calls and excessive usage.
• Akto, Argus overview — 50+ connectors, eBPF, CI/CD red teaming, runtime policies, MCP Gateway and AI Gateway.
• Akto, Agentic Red Teaming — 4000+ probe templates covering the OWASP Top 10 for LLMs.
• Akto, AI Agent Import — agents imported by endpoint URL.
• Redline, Guard benchmark and monitoring documentation.