An agent you can trust with your most sensitive work.
theia
Recent
JR
Jamie Reidacme.com
Investor summary
strahl activegpt-5.5
A plug-in integration in a few lines of code. No model to retrain, no rebuild, and no change to the stack you already run.
# install
pip install strahl
# label each message role
strahl.set_role_labels({
"user": Label(source={"user"}, visibility={"user"}),
"assistant": Label(source={"assistant"}, visibility={"user"}),
})
# declare the flow policy
@strahl.tool(requires=Label(source={"user"}))
def send_email(to, subject, body): ...
# analyze before the tool executes
analysis = strahl.analyze(messages)
analysis.raise_if_denied()Centralized controls for all of your agent deployments. strahl works across vendors, families and harnesses.
Most teams run several agent stacks at once. strahl secures all of them from a single place, instead of re-implementing policy tool by tool across every team and framework.
strahl picks up your existing metadata & permissions and enforces them at the agent level, so an agent can never reach beyond what its user is already allowed to touch.
A few lines around your existing agent. No model to retrain, no rebuild, no new framework to adopt — the agent keeps its code and gains a control plane.
Enforce your policies through the model without guessing.
AI-based AI safeguards will not hold up in audit environments. strahl provides guarantees on IP access, safety and verifiability you can trust.
Other tools watch an agent from the outside and can be talked past. strahl follows your data through the model itself, so it always knows what came from an untrusted source, and the same trick that fools your agent can’t fool it.
With DORA in force and the EU AI Act phasing in, every block produces an audit-ready record designed for GDPR, the EU AI Act, and internal model-risk reviews, so security and compliance can sign off with confidence.
We're working with teams deploying AI agents in high-stakes settings. Tell us what you're building and we'll show you how strahl fits.