Technical research and infrastructure primitives for agentic systems.
Research papers and architectural specifications that define the foundational infrastructure behind DataGrout. These documents are intended for engineers, platform architects, and technical leadership.
The platform as a whole: one fact substrate under memory, navigation, code, and agents; certified admission and governed re-entry as the kernel; metered credits as the economics; and a context-engineering toolkit above them, where every result is shaped, addressable by handle, and receipted. A map of which paper covers which layer.
Typed interoperability for scalable agent infrastructure. Defines a semantic type system that enables deterministic tool composition across heterogeneous APIs.
Formal verification and cryptographic proofs for agent workflows. Provides provable guarantees that workflows are safe, compliant, and terminable before execution.
Declarative policy layer with Semantic Guards and Dynamic Redaction. Enforces integration allowlists, side effect controls, PII protection, and scope verification at runtime.
Transparent system call interposition with cryptographic policy enforcement. Intercepts agent commands at the OS level, evaluates against signed rule packs, and returns Ed25519-signed allow/deny/redact decisions over mutual TLS.
Orthogonal multi-tier detection combining protocol adherence testing, semantic intent analysis, and deterministic threat pattern evaluation. Includes stateful multi-turn tracking with cryptographically sealed session tokens for fragment assembly and trust erosion detection.
Persistent, namespaced Prolog environments as a platform primitive: certified rule admission with termination classes, a strictly ISO-conformant engine tier with monotonic pinning, and call_tool — inference that suspends mid-proof to dispatch governed platform tools and resumes with the result bound.
Resource governance and cost-aware planning for AI agents. Examines the economic architecture that prevents runaway costs and enables accountable execution.
A virtual budget layer that operates independently of platform billing. Agents receive user-defined budgets in arbitrary units, with estimates before execution and receipts after.
A neuro-symbolic approach to verifying AI-generated code at generation speed. Combines AST fact extraction, LLM intent inference, and Prolog consequence queries to detect semantic violations that static analysis cannot.
An optional module for the Arbiter substrate that splits continuous agent cognition into Reflex (trigger evaluation, zero-token, sub-10ms) and Reflection (agentic loop, LLM-powered) cycles. Eliminates polling through event-driven percepts and achieves 10-100x token cost reduction.
A debugging paradigm where agents plant structured facts during investigation and Prolog performs exhaustive transitive reasoning over the unified fact base. Generalizes Consequential Analysis to cross-domain causal chains spanning code, configuration, and runtime behavior.
The interstitial/breakout cycle: agent work happens outside the context window in typed, cheapest-tier sub-loops, and a single presentation pass surfaces the engineered context. One architecture with two expressions — a platform turn engine and a thin client SDK — meeting in an economic task auction.
A coding agent whose controller is symbolic and whose language model is lateral. Codebases are lensed into queryable fact bases, orientation prevents drift before authorship, models emit end-states rather than diffs, and all work happens in forked workspaces with explicit merge semantics.
Navigation instruments for a model's own conceptual terrain: expand a neighborhood, orient along a semantic axis at a chosen angle, and find the horizon where a framing runs out. A deterministic navigation frame over stochastic content, with maps persisted as symbolic facts.
Workflow construction as a dialog rather than a commitment. A decision tree derived from the symbolic planner is walked one option at a time (present, don't rank) over a mesh of arbitrary size; a completed traversal is minted into a certified, reusable skill.
Continuity treated as a rendering problem, not a storage problem. A durable journal of goal-annotated items is compiled into level-of-detail rungs; hydration renders a budgeted frame per turn with per-fact provenance receipts, and the detail ladder doubles as a disclosure scale for sharing.
A proposed open standard for machine-readable content discovery. Defines a web-native protocol that enables AI agents to discover, index, and consume structured content without scraping.
The following papers are in preparation. Scheduled papers carry a target date; the remaining topics are operational in DataGrout and will be covered in future publications:
Living applications: Logic Cell namespaces served as certified, rationed apps with fact-derived interfaces — October 2026
The latent navigation instruments applied to code — November 2026
Context assembly as a field computation — December 2026
Human-in-the-loop batched approval gates with multi-channel delivery — December 2026
