Secure AI Agents

Solve the AI agent problem you cannot risk getting wrong

Useful AI agents need access. High-stakes agents need boundaries. TypeScope adds type-checked boundaries to your production AI agents without changing your tech stack.

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The Risk

Your AI agent can overreach

Automating real work requires clear limits: what the agent may touch, what it must never touch, and what happens when a prompt or generated code goes wrong.

Prompt injection

A crafted input convinces the agent to act outside its intended task — deleting records, calling unintended APIs, or following the attacker's instructions instead of yours.

Data exfiltration

Generated code can read data it should not access and send it to an external endpoint — sensitive rows, internal secrets, or another customer's records.

Infrastructure damage

Malicious code generated by a compromised prompt can drop database tables, delete files, or wipe valuable data.

The Mechanism

The boundary follows the workflow

Technology matters only after the risk is clear. Once the workflow and protected data are understood, TypeScope moves the security boundary into the type system, proves it at build time, and keeps it compatible with the runtime, API, database, and network defenses you already operate.

Runtime guardrails

  • ✗ Hope a filter catches the bad input
  • ✗ Blind to "only this user's rows"
  • ✗ Violations surface in production

Compile-time boundaries · Jo

  • ✓ Compiler proves the boundary before it runs
  • ✓ Business rules are typed capabilities
  • ✓ Violations are compile errors, not incidents

Multi-layer Defense

Defense in depth: nested confinement zones The AI-generated code sits at the center of three nested walls: Jo's compile-time wall innermost, then runtime isolation, then network policy. Each wall has its own door. An allowed request passes through all three doors; anything else is blocked at the Jo wall before the code runs. Network policy Runtime isolation Jo · compile-time sandbox YOUR DATA & APIS

Jo · compile-time sandbox

The innermost wall. Its door is a typed capability — the compiler proves the agent can reach nothing else, before the code ever runs.

Runtime isolation

The middle wall. Resource quota and filesystem sandboxing — its door is what the OS sandbox permits.

Network policy

The outer wall. Egress policy — its door is the set of endpoints your network allows.

Why TypeScope

For sensitive workflows and data

We ground each build in the workflow, data, and risk your team needs to control. Then we design agents whose limits are provable, auditable, and matched to your security and operational requirements.

Fit before architecture

Before proposing architecture, we map the workflow, the required access, the users, and the failure modes that matter to your team. The system design follows from that understanding.

Your data stays yours

We support in-house systems and cloud deployments. In both cases, data safety is the priority: access is explicit, bounded, and designed around your security requirements.

Auditable

The boundary is readable code, not a black box. Capability limits live in typed interfaces and version control — something a security reviewer or auditor can actually inspect.

Fits your stack

Adopt Jo at agent boundaries. It compiles to Python and Ruby, wraps existing APIs as typed capabilities, and still pairs with runtime isolation, deployment policy, and monitoring.

We also publish exactly what compile-time sandboxing can and cannot guarantee. Transparency is our core value.

The Foundation

Open source, all the way down

TypeScope builds secure AI agents on Jo, our open-source language with compile-time sandboxing. The language, compiler, and security model are all open to inspection — so you can evaluate the approach before you trust it.

Open Source

Evaluate the approach directly

Jo's language, compiler, docs, and case studies are open source. Inspect the security model, run it yourself, and build on a transparent foundation.

View on GitHub

Capability Model

Contextual capabilities

The capability model behind Jo is open source too. Read the contextual-capability work that grounds how authority is passed, narrowed, and checked.

View the model

Have a high-risk AI workflow?

Start with what you want the agent to do, what access it might need, and what would make it unsafe. We will work from there.

Tell us your problem Read the security model