A platform for building reliable AI agents

Architectural standards and best practices for building reliable AI Agents and LLM workflows. Defining the framework for AI Reliability Engineering (AIRE).

The open-source AgentOps evaluation harness.

UAICP (Universal Agentic Interoperability Control Protocol): open reliability contract for AI agent workflows with evidence gating, policy controls, and auditability.

Research archive — four published papers, Mahdi Ledger, and empirical foundations of the LC-OS governance framework.

SpecGuard is a command-line tool that turns AI safety policies and behavioral guidelines into executable tests. Think of it as unit testing for your AI's output. Instead of trusting that your AI will follow the rules defined in a document, SpecGuard enforces them.

Continuity Keys: tests for “same someone” returns. Behavioral identity consistency under pressure. Origin (Alyssa Solen) ↔ Continuum.

ModelPulse helps maintain model reliability and performance by providing early warning signals for these issues, allowing teams to address them before they impact users significantly.

Production-grade TypeScript AI runtime focused on reliability, governance, and reproducible LLM systems. Multi-provider gateway, agents, RAG, workflows, policy engine, audit trails, and deterministic testing — built for teams shipping AI in production.

Precision-Matched Intelligence. The LLM Capability Framework (LCF) is a structural standard for mapping specific task requirements to the most effective model architecture, token strategy, and reasoning depth.

A production-ready framework for evaluating LLM reliability using semantic consistency, vulnerability scoring, and risk-aware trust calibration.

A conceptual AI architecture for reducing hallucinations by enforcing invariant, source-anchored knowledge constraints during generation.

The Guard at the Door - Principle-based AI action blocking

A multi-agent cognitive architecture solving the LLM state-dependency problem with persistent memory and a mandatory self-correction loop. An architecture that is built on a more profound and biologically resonant principle: memory is an active component of intelligence itself.

The Open Source Trust Layer for RAG: Adaptive scoring, decay math, and persistent trust ledgers for more reliable LLM applications

Define reliable AI agent workflows with UAICP, a protocol offering controlled delivery, policy checks, rollbacks, and traceable multi-agent actions.

THE THEMIS PROJECT — Phase I: Reconciling human and digital ontologies. Semantic validity architecture establishing the telos of AGI. For more user friendly information goto: https://echosphere.io

Reliability first AI debriefing demo with traceable outputs, schema validation, and evaluation scoring.

Control Plane for AI Decision Reliability

Reliability testbed for LLM pipelines: RAG, tool-calling, red-teaming, drift monitoring and deterministic evaluation.