Systems | Deep Bound Research

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System Landscape

15 systems organized across five tiers: core flagships, tier-1 products, infrastructure primitives, evaluation harnesses, and research programs. Each operates under the Deep Bound Research governance framework.

Retro workstation rendering the Deep Bound system landscape

Release Disclosure

Deep Bound Research currently publishes public-safe research papers, governance drafts, technical notes, system maps, and design-space artifacts.

Core systems including Ex1, Boundary, Plateau, ACE, Cerberus, and related harness infrastructure are not publicly released unless explicitly marked with: Live Repository, Public Release, Pilot Access, or Open Source Availability.

Public pages describe research direction, governance models, operational principles, and public-safe architectural abstractions. Internal implementation details remain private until reviewed for safety, stability, and release readiness.

5

Core Flagship Systems

1

Tier-1 Products & Systems

4

Infrastructure Systems

3

Evaluation + Governance

2

Research Programs

01

Core Flagship Systems

The canonical five flagship systems. Each represents a distinct layer of the lab's public architecture: workspace, simulation, invention, storage, and reasoning.

Controlled Research BetaPublic SiteActive

Ex1

AI Workspace / Operator Product

A public AI-native desktop workspace for sustained work across models, agents, code, tools, projects, tasks, context, approvals, and connectors.

Ex1 gives people a governed surface for running AI work across agents, tools, context, approvals, and recovery points instead of scattering work across disconnected chats, tabs, and ad-hoc automations.

Public site: everythingx1.com

Research PrototypeInternal OnlyActive

Boundary

Simulation & Evaluation Engine

A simulation and synthetic-environment engine for agent rollouts, deterministic replay, evaluation scorecards, and training-dataset export.

Boundary evaluates systems before production exposure: repeatable synthetic environments, traced rollouts, and scored evidence replace anecdotal agent testing.

Internal R&DInternal OnlyDormant

Cerberus

Multi-Head Reasoning & Evaluation Harness

A three-head reasoning and evaluation harness built on structured disagreement: one head proposes, one head critiques adversarially, and one head arbitrates and verifies.

Single-path generation has no internal check. Cerberus makes independent critique and explicit arbitration part of the reasoning architecture, so conclusions arrive with converged evidence instead of unexamined confidence.

Architecture DevelopmentInternal OnlyDormant

StrongHold

Adaptive Storage & Archive Intelligence

A smart storage system for adaptive compression, archival intelligence, retrieval, and restoration — storage that understands artifact structure and can explain its own strategy decisions.

AI-native systems produce artifact streams that opaque object storage cannot serve well. StrongHold selects strategies per artifact, preserves lineage, tracks access telemetry, and plans restoration — not just writes bytes into buckets.

Active Research SystemInternal OnlyActive

Plateau

Invention Studio / Design-Space Intelligence

An invention studio and design-space intelligence system for generating, comparing, scoring, and archiving candidate designs across software, hardware, chips, drones, machines, interfaces, and physical systems before engineering commitments are made.

Plateau gives the lab a governed way to explore what could be built before deciding what should be built. It turns ambiguous ideas into structured candidates that can be constrained, scored, promoted, archived, or rejected — with the design memory preserved either way.

02

Tier-1 Products & Systems

Public-facing products and systems adjacent to the flagship five — governed platforms with their own product surfaces.

Planned ReleasePublic SiteActive

Artchiv

Creative Media Archive & Provenance Platform

The source of truth for creative media — a governed archive for versioned assets, provenance records, retrieval, and inspectable media lineage across studios, agencies, and production workflows.

Creative teams lose time and trust when final files, working masters, approvals, and delivery history live in disconnected drives, chats, and ad-hoc folders. Artchiv gives media a durable, queryable record.

Public site: artchiv.com

03

Infrastructure Systems

Underlying runtime primitives — context engines, routing layers, delegation protocols, and operator bridges that underpin flagship systems.

Open Source PreparationOpen Source PrepMaintained

ACE

Context Engine

A context engine and retrieval infrastructure for task-conditioned retrieval, source ranking and pruning, context budgets, and citation-preserving assembly across AI workspaces and agent workflows.

Treats context as infrastructure: ranks, prunes, and routes sources under explicit budgets so agents see only what is strictly necessary for the current step — with citations preserved.

Research DirectionNot ReleasedActive

X-Router

Capability Routing Layer

A capability-routing layer that selects models, tools, agents, runtimes, and execution paths based on capability, cost, latency, authority, and risk.

AI systems should not treat every task as a default chat completion. Routing work to the right capability under explicit constraints improves reliability, cost control, and operational clarity.

Internal R&DInternal OnlyMaintained

TAG

Typed Agent Gateway

A typed delegation protocol that models every handoff between agents, tools, models, and humans as a structured object with explicit authority, scope, budgets, and evidence contracts.

Reliable agent systems need more than loose prompt chaining. TAG treats delegation as a governed protocol: every delegated task carries its principal, authority scope, budget, and evidence contract with it.

Architecture DevelopmentInternal OnlyMaintained

GOC

Governed Operator Bridge

A governed bridge for controlled interaction with CLI-first, browser-based, legacy, and other non-API software surfaces.

Many useful tools do not expose clean APIs. GOC is the controlled bridge into those software surfaces — observable, permissioned, and recoverable — distinct from broader runtime research (AMR) or an autonomy substrate.

04

Evaluation + Governance

Harnesses and oversight systems for measuring agent behavior, long-horizon reliability, and supervisory runtime control.

Internal R&DInternal OnlyActive

M-Class Harness

Governed Evaluation Methodology

A governed evaluation methodology, scoring framework, and review standard for coding, reasoning, and multi-step AI workflows.

As AI agents move beyond single responses, evaluation must measure sustained work: state tracking, tool use, failure recovery, handoff quality, and evidence trails — under a consistent, governed scoring standard.

Internal R&DInternal OnlyMaintained

Long-Horizon Harness

Extended-Task Execution Environment

An execution and evaluation environment for extended tasks involving interruptions, memory degradation, state change, recovery, and handoffs.

Short benchmarks miss the failure modes that appear in real work. The Long-Horizon Harness is the environment where sustained execution actually runs — interruptions, degraded memory, changed state, and handoffs included.

ConceptNot ReleasedReference Only

TripSitter

Supervisory Runtime

A supervisory runtime for active monitoring of autonomous AI sessions: uncertainty detection, drift detection, budget and policy monitoring, and human escalation with bounded intervention.

Autonomy requires active supervision. Where M-Class scores work after the fact and the Long-Horizon Harness provides the environment, TripSitter watches sessions while they run and returns control to humans before failures compound.

05

Research Programs

Research initiatives studying distributed cognition, controlled machine interaction, and agent-software runtime boundaries.

Research DirectionNot ReleasedActive

DCS

Distributed Cognitive Systems

A research program studying cognition distributed across humans, models, agents, operators, tools, memory stores, machines, and control planes — and the authority structures that govern it.

Real AI-native organizations are not single agents. Understanding how distributed cognition composes — and how authority, coordination cost, and failure propagate through it — is foundational to everything the lab builds.

Research PrototypeInternal OnlyMaintained

AMR

Agent-Machine Runtime Research

A research program for connecting AI agents to controlled machine interfaces while preserving observability, permission boundaries, and recoverable execution.

Many real workflows happen outside clean APIs. AMR studies how agents can interact with operational software without losing auditability or control — the broader runtime research that bridges like GOC plug into.

Research Structure

Deep Bound Research uses a staged R&D structure. Early projects begin in a private founder workspace. Reusable research infrastructure is extracted into Deep-Bound-Research. Flagship systems live under House-Labs. Each system is governed by the Deep Bound Research maturity framework.

Concept → Research Direction

Exploration phase

Research Prototype → Internal R&D

Development phase

Public Draft → Operational

Release phase