Robust Intelligence Alternative - Runtime, Not Just Testing
Robust Intelligence is strong at AI validation and testing. Agentshield is the alternative that stays in the live action path, blocking and gating actions in production.
Direct answer
A Robust Intelligence alternative in 2026 means an alternative to Cisco AI Defense, since Robust Intelligence is no longer sold standalone. Be careful with older comparisons, including our own earlier version of this page: Cisco AI Defense is not validation-only any more. At Cisco Live EMEA on February 10, 2026 Cisco added real-time agentic guardrails that "continuously monitor and inspect agentic interactions to detect manipulation or unsafe behavior," plus in-path MCP visibility and policy control through AI-aware SASE. That is genuine runtime enforcement. The real difference now is scope and buying model: Cisco governs AI across an estate through an enterprise sales cycle, while Agentshield is a self-serve, stack-neutral control plane you put in front of one agent, enforcing tool and data permissions, holding high-risk actions for approval, and writing an immutable audit trail.
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Where Robust Intelligence is strong
Robust Intelligence is well known for AI model validation, testing, and risk assessment, helping teams find weaknesses before deployment.
Where Agentshield is the alternative
Agentshield is a self-serve, vendor-neutral control point for one agent action path. Cisco AI Defense is a platform play priced and sold for governing AI across a whole enterprise estate. Since February 2026 it also enforces at runtime, so the honest wedge is scope and rollout, not testing versus enforcement: we are the narrow tool you deploy this week without a Cisco commitment.
Side by side
Agentshield vs Robust Intelligence, honestly.
| Dimension | Agentshield | Robust Intelligence |
|---|---|---|
| Runtime protection | Enforces in the live action path in production. | Also enforces at runtime since Feb 2026: real-time agentic guardrails plus in-path MCP policy control. |
| Red teaming | Built-in attack runs, then runtime enforcement. | Stronger here. Advanced algorithmic red teaming, adaptive single and multi-turn, multiple languages. |
| Tool permissions | Per-agent least-privilege scopes at the boundary. | Governs MCP and tool use at platform scale rather than per-agent scopes. |
| Audit trail | Immutable record of every production action. | Reporting around assessments. |
| Pricing | Self-serve, on the page. | Enterprise, sales-led. |
Comparison reflects our understanding of publicly available information and is meant to be fair. Vendors evolve; verify the latest before deciding.
Robust Intelligence is now Cisco AI Defense
The first thing to know in 2026: Robust Intelligence is no longer sold as a standalone product. Cisco acquired it in October 2024 in a deal reported around 400 million dollars, and its technology now sits inside Cisco AI Defense, part of the Cisco Security Cloud. Robust Intelligence pioneered algorithmic red teaming and an early AI firewall, and that IP is real, but you now buy it as Cisco AI Defense through Cisco enterprise channels.
Cisco AI Defense is strong at what Robust Intelligence was strong at: pre-deployment validation, algorithmic red teaming, and AI supply-chain scanning, now extended in 2026 with an AI Bill of Materials, an MCP catalog, and real-time agentic guardrails. It is an enterprise platform play, tied to the Cisco stack and sold through sales.
Agentshield sits in a different place: the live action path of a specific agent, self-serve and vendor-neutral, deployable without a platform commitment.
What Cisco shipped in 2026, and where that corrects our own comparison
We are going to correct this page against ourselves, because the alternative is leaving an inaccuracy up that happens to flatter us. Earlier versions of this comparison framed Cisco AI Defense as validation and testing while positioning Agentshield as the runtime option. As of February 2026 that framing is wrong. Cisco announced the largest update to AI Defense since launch at Cisco Live EMEA in Amsterdam on February 10, 2026, and several of the new capabilities are squarely runtime enforcement.
| Capability announced Feb 10, 2026 | What Cisco says it does | Honest read for a buyer |
|---|---|---|
| Real-time agentic guardrails | "Continuously monitor and inspect agentic interactions to detect manipulation or unsafe behavior," giving poisoned tools and poisoned prompts as the examples | This is runtime enforcement on the agent path. It overlaps our core claim directly, and pretending otherwise would not survive a demo |
| AI BOM (bill of materials) | "Provides centralized visibility and governance for AI software assets, including model context protocol (MCP) servers and third-party dependencies" | We do not do estate-wide asset inventory. If that is your requirement, Cisco is the answer and we are not |
| MCP Catalog | "Discovers, inventories, and helps manage risk across MCP servers and registries spanning public and private platforms" | Discovery across an estate at network scale is a Cisco strength that is hard to replicate without their footprint |
| Advanced algorithmic red teaming | "Adaptive single and multi-turn testing for models and agents in multiple languages" | The original Robust Intelligence IP, extended. Genuinely stronger than our red teaming at model level |
| AI-aware SASE: MCP visibility, logging and policy control | "Discovers and governs MCP communications with in-path controls and inspection outcomes," alongside intent-aware inspection of agentic messages and actions | In-path control is the same architectural position we occupy, delivered through their network layer. A real overlap for Cisco-standardized shops |
Cisco added more at RSA Conference 2026 with DefenseClaw, which bundles open source tooling including a skills scanner, an MCP scanner, AI BOM and CodeGuard, aimed at scanning and sandboxing skills and verifying MCP servers. Taken together, 2026 was the year Cisco stopped being a pre-deployment testing story.
So what is left of our argument? Scope and rollout, which is a narrower claim than the one this page used to make and a more defensible one. Cisco AI Defense governs AI usage across an organization, priced and sold accordingly, and it is worth the sales cycle when that is the problem. Agentshield is a single control point in front of one agent action path, with per-agent permission scopes, approval gates on irreversible actions, and an audit trail, deployable today without adopting a platform. If you are evaluating both, the deciding question is not who enforces at runtime, because both now do. It is whether you are buying an estate program or a control for a specific agent that holds credentials. Our wider read of the vendor landscape is in AI agent security companies.
Testing before launch versus enforcing in production
Robust Intelligence built its reputation on finding weaknesses before a model ships: stress-test the model, red-team it, score the risk, then deploy. That is valuable, and Agentshield includes red-teaming for the same reason. The gap is what happens after launch. A validated model still receives a poisoned web page, a malicious tool result, or an instruction buried in a document at runtime.
Agentshield stays on in production. It blocks prompt injection at execution time, denies out-of-policy tool calls, holds refunds, deletes, and sends for human approval, and records every action immutably. The clean pattern is to test with red-teaming and then keep a runtime firewall enforcing, rather than treating a passing pre-launch report as the finish line.
Where Cisco AI Defense is the better buy, and where it is not
Cisco is a serious competitor here and this comparison is written to be useful rather than flattering. Cisco AI Defense organizes itself around three outcomes: discover AI applications, models, agents and shadow AI activity; detect unsafe models, vulnerable applications and prompt injection exposure; and protect with runtime guardrails on prompts, responses, agent actions and MCP interactions. Cisco has kept investing, announcing further agentic capabilities through 2026 including DefenseClaw, which scans and sandboxes agent skills, verifies MCP servers, and inventories AI assets automatically.
| What you need | Better fit | Why |
|---|---|---|
| Estate-wide discovery of shadow AI across a large enterprise | Cisco AI Defense | Discovery at network scale is a Cisco strength and it plugs into hardware and SASE you may already run. We do not compete on network-layer visibility |
| Pre-deployment model validation and algorithmic red teaming | Cisco AI Defense | This is the original Robust Intelligence IP and it is genuinely good. We red-team agents, but model-level validation is not our center of gravity |
| You are already standardized on Cisco Security Cloud | Cisco AI Defense | Single vendor, single contract, shared policy surface. That is a legitimate reason to choose it and we will not argue with it |
| Runtime control over a specific agent your team built | Agentshield | We sit in that agent action path with per-agent permission scopes and approval gates, rather than governing AI usage at the platform level |
| Starting this week without an enterprise sales cycle | Agentshield | Self-serve and stack-neutral. Cisco AI Defense is bought through enterprise channels, which is the right model at its scale and the wrong one for a team shipping an agent next sprint |
The short version: if your problem is governing all AI use across a large organization, Cisco has the broader answer. If your problem is that a specific agent holds credentials and makes autonomous tool calls, that is the problem we are built for. Teams comparing more of the market should read our writeup of AI agent security companies.
FAQ
Common questions.
Does Cisco AI Defense do runtime protection or only testing?
Both, as of 2026. Older comparisons, including earlier versions of this page, described it as pre-deployment validation. On February 10, 2026 Cisco added real-time agentic guardrails that continuously monitor and inspect agentic interactions to detect manipulation or unsafe behavior, plus in-path MCP policy control through AI-aware SASE. Treat any source calling it testing-only as out of date.
What is the difference between Agentshield and Cisco AI Defense?
Scope and buying model, not testing versus runtime, since both now enforce at runtime. Cisco AI Defense governs AI across an enterprise estate, with discovery, an AI bill of materials and an MCP catalog, bought through enterprise channels. Agentshield is a self-serve, stack-neutral control point in front of one agent, with per-agent permission scopes, approval gates and an immutable audit trail.
Is Cisco AI Defense a good Robust Intelligence replacement?
It is the direct continuation rather than a replacement, since the Robust Intelligence technology became its foundation. If you valued the red teaming and validation, that capability carried over and Cisco has kept extending it through 2026 with agentic guardrails, MCP server verification and AI asset inventory. The change is commercial: enterprise platform, enterprise sales cycle.
Does Cisco AI Defense cover AI agents or only models?
Both, and increasingly agents. Cisco describes runtime guardrails applying to prompts, responses, agent actions and MCP interactions, alongside discovery of agents and frameworks. The distinction worth probing in an evaluation is whether controls attach to each agent identity and its permitted tools, or to AI traffic in general, because those answer different questions during an incident.
Does Robust Intelligence still exist as a standalone product?
No. Cisco acquired Robust Intelligence in October 2024 and folded its technology into Cisco AI Defense, part of the Cisco Security Cloud. The standalone product is no longer sold on its own. Evaluation, purchase, and support now run through Cisco enterprise channels rather than a separate Robust Intelligence contract.
What is Cisco AI Defense?
Cisco AI Defense is Cisco AI security product, built substantially on Robust Intelligence technology. It centers on algorithmic red teaming, model and application validation, AI supply-chain scanning including model files and MCP servers, an AI Bill of Materials, and real-time agentic guardrails, integrated with the Cisco Security Cloud.
What is the difference between validation and runtime enforcement?
Validation tests a model or agent before deployment to find weaknesses, which is where Robust Intelligence excelled. Runtime enforcement stays in the live request path and blocks attacks as they happen. Agentshield does both: it red-teams your agent and then keeps a firewall enforcing permissions and blocking injection in production.
Is there a self-serve alternative to Cisco AI Defense for agents?
Yes. Agentshield is a vendor-neutral, self-serve runtime control plane you can deploy in front of any agent without a Cisco platform commitment or a sales cycle. It blocks prompt injection, enforces least-privilege tool and data permissions, gates high-risk actions, and keeps an immutable audit trail, with prices published on the page.
What are the best Cisco AI Defense alternatives?
The closest Cisco AI Defense alternatives are Prisma AIRS from Palo Alto Networks, HiddenLayer, Check Point AI security, which now carries Lakera, and the independents Noma Security and Pillar Security. For agent runtime enforcement specifically rather than model validation, the comparison set narrows to Agentshield and Zenity.