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AGIACC.

Trustworthiness-native AI

Trustworthiness,
built into
the AI stack.

AGIACC is building toward a trustworthiness-native AI solution—from chip design and new devices, through the system software stack, to application frameworks, SDKs, platforms and services. Our first step is a reference platform for enforceable access limits, isolated execution and predictable failure behavior.

Explore the platform Read the research
Today Research & artifactsNext Reference platform + SDK
AGIACC / ARCHITECTUREPlatform direction
  1. 04
    AI applications & servicesAgents · training · inference
  2. 03
    Frameworks & SDKsPermissions · platforms · services
  3. 02
    System software stackIsolated execution & recovery
  4. 01
    Hardware foundationsChips · devices · ISA
Trust boundaries, connected across the stack.
Designed for consequential AI

Security and execution safety set the constraints. Efficiency makes them practical.

01 / The problem

More capability.
Stronger boundaries.

Agents now execute code, invoke tools, and act on sensitive information. One weak boundary can turn a local fault into a wider system failure.

01 — AUTHORITY

What may it access?

Untrusted tools and broad permissions create opportunities to reach unrelated data and resources.

Limit authority.
02 — MEMORY

Which operations are valid?

Memory corruption can cross the boundaries applications assume will hold.

Enforce valid access.
03 — FAILURE

Where does a fault stop?

Connected components need explicit isolation and documented behavior when something goes wrong.

Contain the consequences.
04 — ADOPTION

Can teams use it?

Protection has to survive real software, hardware constraints, and integration work.

Measure the tradeoffs.

02 / Our design approach

Contain the impact.
Prevent the invalid.

Two complementary lenses for security and execution safety.

Passive defense

Isolate and contain.

Restrict the reach of untrusted components. Use boundaries to limit the consequences of faults and compromise.

Examples: sandboxing, compartmentalization, limited interfaces.

Active defense

Enforce valid operations.

Build rules into the execution path so invalid access can be rejected at the boundary where it matters.

Examples: memory protection, capabilities, permission checks.

These mechanisms overlap. Security and safety are distinct outcomes, supported by a connected hardware and software design.

Understand our approach

03 / The reference platform

One stack.
Explicit boundaries.

Platform + SDK planned

For systems, platform, and security engineers responsible for consequential AI deployments.

Explore the planned platform
Planned architectureSelect a layer to explore
Hardware / software co-design

Hardware foundations

Software depends on the boundaries the machine can actually enforce.

Intended protection
Explore memory-access bounds, permissions, and compartment boundaries through hardware/software co-design. Guarantees depend on the architecture, toolchain, allocator, and operating system.
Research direction
Explore how chip design, memory protection and new device interfaces can establish execution boundaries that remain usable by system software.
Next evidence
Document the hardware and software assumptions of a reference example on existing hardware or emulation.
Explore this direction
Passive containment + active prevention

System software stack

A faulty or compromised tool can affect other components when isolation and authority are too broad.

Intended protection
Plan explicit resource boundaries, limited inter-component communication, and fault handling. Sandboxing and fine-grained compartments are complementary directions.
Research direction
Connect isolation, authority, scheduling and recovery into a coherent runtime contract, with efficient execution inside the protection constraints.
Next evidence
Reproduce permitted and rejected tool operations, with documented fault reporting and containment limits.
Explore this direction
Planned developer interface

Frameworks & SDKs

Application intent must translate into enforceable permissions across the underlying stack.

Intended protection
Plan interfaces that express permitted resources, mediate tool access, and report accepted operations, denials, and failures. Enforcement remains in the underlying components.
Research direction
Develop application interfaces for permissions and observable execution, beginning with an SDK and exploring frameworks and platform services as the solution grows.
Next evidence
Publish a documented example that another engineer can run and evaluate.
Explore this direction
Cross-industry applications

AI applications & services

Teams need to understand whether their full workflow retains its boundaries, correctness, and practical usability.

Intended protection
Evaluate representative tool-using agents and AI services against an explicit threat model, then measure deployment costs within those constraints.
Research direction
Evaluate representative agent, training and inference workflows across the hardware, system software and application interfaces.
Next evidence
Evaluate selected partner workflows; report protection tests, integration effort, and workload-specific performance.
Explore this direction

A platform direction grounded in research. Individual mechanisms have specific limits; the combined platform and SDK are planned.

04 / From design to evidence

A tool-using agent.
A boundary you can inspect.

Our first planned reference example follows one task through application intent, runtime enforcement, and observable outcomes.

Illustrative reference workflow

  1. Define the task

    An agent reads an approved document or maintenance log and proposes a next step.

  2. Express authority

    The application grants a tool access to the required resources and states its limits.

  3. Enforce boundaries

    The runtime mediates access; the platform’s memory protections apply within their stated scope.

  4. Record the outcome

    Report permitted operations, rejected access, and faults, with evidence for evaluation.

Intended observable outcomes
  • Permitted work completes
  • Unrelated access is rejected
  • Faults are contained and reported

Architecture illustration for a planned reference example. Industrial examples use approved data and proposed actions; they do not imply an existing deployment or certified control system.

Illustrative deployment contexts

Oil & gas · Industrial operations · Robotics · Embedded systems · Enterprise infrastructure

05 / Research directions

The foundations
for useful autonomy.

We study how AI systems can complete useful work within clear limits on access, failure and resources. That takes us from chip and device design to runtimes and the tools developers use.

Explore the research directions

Our first reference platform will bring these questions into a workflow that developers can run, inspect and extend.

06 / The path ahead

Research today.
Deployment through evidence.

Begin with existing hardware or emulation. Build an evaluable platform and SDK. Let real deployment requirements guide deeper co-design.

View the complete roadmap
  1. TodayExisting work

    Research foundations

    Published research and available artifacts in memory protection, sandbox orchestration, and AI systems efficiency.

  2. 0–3 monthsPlanned

    Reference demonstration

    Connect a defined threat model, existing hardware or emulation, runtime isolation, and explicit application permissions.

  3. 3–6 monthsPlanned

    Reference platform + SDK

    Package developer examples, permission interfaces, fault reporting, and evaluation tooling.

Proposed horizons from project kickoff; advancement depends on evidence and resourcing.

Proposed horizons from project kickoff. Future stages depend on reproducibility, customer evaluation, and available resources.

ABOUT / AGIACC

Computer systems research.
Hardware-to-software thinking.

07 / About us

From systems research.
Toward trustworthy AI.

Founded by Xiaoyang Sun, AGIACC brings a systems perspective to trustworthy AI, drawing on experience in secure execution, hardware memory protection and efficient AI infrastructure.

Xiaoyang’s academic research, industry collaborations and experience co-founding ACE3 shape our approach: connect strong technical foundations with the practical needs of people building AI systems.

Get to know AGIACC

Build the next stage with us

Where does your AI
need a stronger boundary?

We welcome design partners, research collaborators, and investors who see trustworthiness as a foundation for capable AI.

Start a conversation Read the company brief