Agentshield

Runtime firewall · AI agent security

AI agent security that ships agents to production without getting breached.

Agentshield is the runtime firewall for AI agents. It blocks prompt injection, enforces tool and data permissions, and logs every action. Drop it in front of any agent.

Governing agent traffic for teams in production · 14M+ actions governed · we never train on your data

Threat Console
12,408 injections blocked this week

Run a request

Inspection lane

INSPECTING
untrusted input

Policy trace

High-risk action held for approval

Audit trail

Drops in front of

OpenAI Anthropic LangChain CrewAI MCP Custom agents
OWASP LLM Top 10 EU AI Act-ready SOC 2 in progress Never trains on your data

The problem

Your agent reads untrusted text, then acts with real credentials.

Autonomous agents went from demos to production in eighteen months. They read web pages, emails, documents, and tool outputs, then call APIs, move money, edit code, and touch customer data. That is a new, ugly attack surface.

Prompt injection

A poisoned page or email plants hidden instructions and hijacks the agent into doing the attacker's bidding.

Over-permissioned agents

An agent with broad access can wipe a database, exfiltrate data, or trigger a destructive action it never should have reached.

No audit trail

When something goes wrong, most teams cannot say which agent did what, on whose instruction, or what it touched.

Agentshield is the control layer that makes shipping agents safe. Calm operator, loud alarm.

How it works

Intercept, inspect, enforce, record.

Agentshield sits in line on the agent request and action path. Every instruction it reads and every tool it calls passes through the inspection line first.

Step 01

Intercept

Agentshield sits in line on the agent request and action path. Every instruction the agent reads and every tool call it tries to make passes through the inspection line first.

Step 02

Inspect

Each request is scanned for prompt injection, data-loss risk, and policy violations. Untrusted input is treated as untrusted, and the tool, resource, and payload are checked against the rules you wrote.

Step 03

Enforce

In-policy actions are cleared and proceed. Out-of-policy or injected actions are blocked at execution time. High-risk actions are held for a human to approve or deny. Least-privilege by default.

Step 04

Record

Every action and decision is written to an immutable, tamper-evident audit trail: who invoked the agent, which tool, which resource, the verdict, and the timestamp. Searchable and exportable to your SIEM.

Stack-neutral

Drops in front of anything.

No framework rewrite. Agentshield is a control plane, not a framework, so it sits in front of the stack you already built and adds firewall, permissions, monitoring, and audit.

OpenAI Anthropic LangChain CrewAI MCP Custom agents OpenAI Agents SDK Vercel AI SDK Any HTTP tool

From the field

What teams shipping agents tell us.

“We were stitching a prompt-injection filter, a permissions hack, and a homegrown log together before this. Agentshield was the one control plane we did not want to maintain ourselves. It caught an injection in a vendor email on day two of the pilot.”
MF Marcus Feld Platform Lead, autonomous ops company
“The audit trail is what got us through procurement. Every agent action ties back to who invoked it, which tool it called, which resource it touched, and the policy verdict. That is the exact evidence the EU AI Act reviewers asked for.”
DW Dana Whitfield Security Engineer, vertical AI startup
“Stack-neutral mattered to us. We kept our LangChain and MCP setup and just put Agentshield in front. Least-privilege tool permissions plus a human gate on destructive calls let us turn the agents loose without holding our breath.”
PR Priya Raman Staff Engineer, coding-agent platform

Pricing

Transparent pricing. No contact-sales wall.

Developer

Solo builders shipping their first production agent

$39 /mo

Billed yearly

.

  • Up to 3 agents
  • Prompt-injection firewall
  • Tool and data permissions
  • Real-time action monitoring
  • 30-day immutable audit trail
  • OpenAI, Anthropic, LangChain, MCP
Most popular

Team

Teams running agents in production

$159 /mo

Billed yearly

.

  • Up to 25 agents
  • Everything in Developer
  • Human-approval gates
  • Data-loss prevention rules
  • 1-year audit trail and SIEM export
  • Observe-only and enforce modes

Scale

Platforms governing fleets of agents

$599 /mo

Billed yearly

.

  • Unlimited agents
  • Everything in Team
  • Custom policy packs and red-team runs
  • Multi-workspace and roles
  • SAML SSO and audit export API
  • Priority support and SLA

Enterprise

Regulated and high-volume deployments

Custom

Billed yearly

.

  • Custom volume pricing
  • On-prem, VPC, or private cloud
  • SOC 2 report and DPA
  • OWASP LLM and EU AI Act evidence pack
  • Custom approval workflows
  • Named security engineer

All plans are paid. The interactive demo is free to try. Prices in USD. See full pricing.

FAQ

Questions developers and security buyers ask.

AI agent security is the practice of protecting autonomous AI agents from being hijacked and stopping them from doing damage when they act with real credentials. Because agents read untrusted text (web pages, emails, documents, tool outputs) and then call tools, move data, and take actions, they introduce a new attack surface: prompt injection, over-permissioned access, and missing audit trails. Agentshield is the runtime firewall for AI agents that addresses all of it in one control plane: it blocks prompt injection, enforces exactly which tools and data each agent may touch, monitors every action in real time, and writes an immutable audit trail.

For the common path, inspection is millisecond-class and runs inline so the agent waits only a moment before its action is cleared, blocked, or held. For latency-sensitive flows you can run Agentshield in observe-only mode, where it monitors and logs every action without blocking, then switch specific high-risk tools to enforce mode once you trust the policy. You decide which actions are inspected inline and which are watched asynchronously.

No, when policy is scoped to what each agent actually needs. Agentshield is tunable and starts in observe-only mode so you can see what would be blocked before anything is. Decisions are policy-scoped rather than a blanket filter, so a clean, in-policy action sails through. The Clean request scenario in the demo shows a legitimate action being cleared, which is the point: Agentshield governs traffic, it does not just block everything.

Agentshield is stack-neutral. It drops in front of OpenAI, Anthropic, LangChain, CrewAI, MCP servers, and custom agents as a control plane, so there is no framework rewrite. It sits in the request and action path rather than replacing your agent framework, which means you keep the stack you already built and add firewall, permissions, monitoring, and audit in front of it.

No. Agentshield never trains on your data, ours or anyone else's. It inspects agent traffic to enforce policy; it does not collect your content to build models. Data is encrypted in transit and at rest, access is least-privilege, and you control retention and deletion. You stay in control of every action. See the security page for full data-handling detail.

No. Agentshield is four controls in one plane, not a single guardrail. It combines a prompt-injection firewall, tool and data permissions, real-time monitoring, and an immutable audit trail, so you get the firewall plus least-privilege plus observability plus the audit evidence without stitching together three vendors. It is the control layer in front of your agent, not a single point tool you bolt on.

Yes. The human-approval gate, shown in the demo, holds high-risk actions such as refunds, deletes, mass sends, or data exports for a person to approve or deny before they execute. You set which tools and thresholds require a human, and the agent waits until someone signs off. Every approval is written to the audit trail with who approved and when.

Yes. Agentshield's policy controls and immutable audit trail map directly to the evidence that frameworks and procurement now ask for. The OWASP Top 10 for LLM Applications is the canonical risk taxonomy, and the EU AI Act's high-risk obligations require records of what automated systems did. The audit trail is exactly the artifact auditors and enterprise buyers request, and it exports to your SIEM.

Yes, on higher tiers. On-prem, private cloud, and VPC deployments are available on Scale and Enterprise plans for teams with data-residency or isolation requirements. Reach out through the contact page and a security engineer will scope the deployment with you.

Agentshield has transparent, self-serve pricing with real prices on the pricing page, starting at $49 per month on the Developer plan, $199 per month on Team (the most popular), and $749 per month on Scale, with Enterprise priced by volume. There is no contact-sales wall to learn the cost, which is part of the wedge: you should not have to book a call to find out what an agent security tool costs.

More questions? Read the full FAQ or contact us.

Stop hoping. Start governing.

Stop hoping your agents behave. Start governing them.

Put Agentshield in front of your agents and watch it block an attack live. Real prices on the page, no contact-sales wall.