About AgentGuards
AgentGuards was built by engineers who spent years working on AI infrastructure and kept running into the same problem: AI agents are powerful, but they are also extremely easy to manipulate.
A single injected instruction — hidden in a file, a web page, or a user message — can redirect a coding agent to exfiltrate credentials, delete files, or call endpoints it was never meant to touch. The model has no way to tell the difference between a legitimate instruction and a malicious one embedded in its context.
We built AgentGuards because we wanted a guardrail layer that was fast enough not to matter, strong enough to actually block attacks, and simple enough to add in an afternoon.
Why we built it
AI coding agents — Claude Code, GitHub Copilot, OpenAI Codex — run with real credentials. They have access to your terminal, your source code, your environment variables, and your git history. They can open ports, push commits, and call external APIs.
That is a large attack surface. And it is largely unguarded.
Prompt injection is not a theoretical risk. Researchers have demonstrated attacks that redirect agents mid-task, extract secrets from context, and exfiltrate data to attacker-controlled endpoints — all via crafted input that looks harmless to a human reader.
Existing defences (system prompts, custom instructions) ask the model to police itself. That does not work against a sufficiently crafted attack — the model is the target, not the defender.
AgentGuards sits outside the model. It checks every prompt before the model ever sees it. If the input is malicious, the model never processes it.
Who it is for
AgentGuards is useful any time a model processes untrusted input.
Developers using AI coding agents
You run Claude Code, GitHub Copilot, or OpenAI Codex with access to your terminal, files, and credentials. One injected instruction can do real damage.
Teams building LLM-powered products
Your app accepts user input and passes it to a model. You need to know that neither your users nor the content they feed in can hijack the model's behaviour.
Engineering leaders and security teams
You need an audit trail, configurable policies, and the ability to demonstrate that AI usage in your organisation meets your security standards.
Enterprises adopting AI at scale
Multiple teams, multiple agents, multiple API keys. You need per-tenant control without asking every team to re-implement guardrails from scratch.
What we believe
Guardrails should be invisible when not needed
Clean traffic flows through unchanged. The check adds under 50 ms. You should never know AgentGuards is there — unless it catches something.
Security should not require cooperation
Hooks and proxies enforce checks at the network or OS level — the model cannot bypass them even if instructed to. Cooperative-only approaches (custom instructions, system prompts) are not enough.
Operators should stay in control
You configure which checks run, tune thresholds, add your own patterns, and set policies. We provide the engine; you set the rules.
No data retention without consent
Prompts are evaluated and discarded. We do not store prompt content, train on it, or sell it. What your agents process is your business.
Company
Address
AgentGuards3 Eucalyptus St.
Ramat Yishai, Israel
Ready to add guardrails?
Free tier includes 5,000 checked requests per month. No credit card required to start.