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A11oy Research & Reference Lineage
Purpose
This document traces which open-source projects and publications influenced A11oy's design, what specific idea each contributed, and how we re-implemented it in our own voice.
Agentic AI Runtimes & Orchestration
Anthropic Constitutional AI / Responsible Scaling Policy
- Link: https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback
- Idea borrowed: The notion of a constitution as a hierarchical set of principles that govern model behavior at inference time, not just training time.
- Our implementation: A11oy's Constitution is a runtime-enforced contract, not just documentation. Every agent action cites which article it honored or refused. We extended this with the new Adversarial Covenants chapter (Article IX) that governs what the predictive swarm is allowed to simulate — matching Anthropic's responsible scaling commitments but in an operational, proof-chained form.
Anthropic Mythos / Agent Research
- Link: https://www.anthropic.com/research (agent architecture papers)
- Idea borrowed: Structured tool use, multi-step reasoning with intermediate artifacts, and the idea of a "proof chain" that lets humans audit each reasoning step.
- Our implementation: Praxis (A11oy's AI assistant) emits a proof packet for every answer — model, lane, latency, cost, tool calls, trust score. The Praxis toolbar implements named first-class actions (Analyze, Search the Web, GitHub) rather than unlabeled icons.
LangGraph (LangChain)
- Link: https://github.com/langchain-ai/langgraph
- Idea borrowed: Cyclical graph-based agent workflows where state is passed explicitly between nodes, enabling loops, branches, and human-in-the-loop checkpoints.
- Our implementation: A11oy's Playbook Engine uses a similar node graph model (trigger → action → decision → gate → HITL → remediation) but adds covenant policy enforcement at each node boundary. Every policy-gated node produces a proof ledger entry.
Microsoft AutoGen
- Link: https://github.com/microsoft/autogen
- Idea borrowed: Multi-agent conversations where specialized agents collaborate, with a human-proxy pattern for HITL integration.
- Our implementation: A11oy's Swarm Orchestrator assembles agent pools with explicit role assignments, bilateral scope approval for cross-agent tool sharing, and intelligence compartmentalization. The "governed adversary loop" in the Predictive Defense Cortex adapts AutoGen's multi-agent pattern with constitutional covenant gates.
CrewAI
- Link: https://github.com/joaomdmoura/crewAI
- Idea borrowed: Task decomposition across a crew with defined roles — analyst, researcher, writer. Sequential and parallel task execution with crew-level context sharing.
- Our implementation: The Swarm Orchestrator's mission model (each agent has a role, progress bar, and inter-agent intelligence graph) is inspired by CrewAI's crew concept, but adds proof-ledger logging for every intelligence-sharing event.
OpenDevin / SWE-agent
- Link: https://github.com/OpenDevin/OpenDevin
- Idea borrowed: Agentic code execution with sandboxing, environment state, and iterative action-observation loops.
- Our implementation: A11oy's Praxis GitHub tool operates in a read-only inspection mode — it can search repos and pull file context but cannot write code without explicit HITL approval, honoring Article II of the Constitution.
Letta (MemGPT)
- Link: https://github.com/cpacker/MemGPT
- Idea borrowed: Agents with hierarchical memory — in-context, archival, and recall — enabling persistent, long-running agent identities.
- Our implementation: A11oy's memory layer separates agent working memory from the archival proof ledger. The Adversary Swarm agents maintain per-run memory of the digital twin's state while their outcomes are persisted in the cortex prediction database.
DSPy
- Link: https://github.com/stanfordnlp/dspy
- Idea borrowed: Declarative, optimized prompt programming — signatures, modules, and automatic optimization.
- Our implementation: A11oy's model router uses a lane-based dispatch (fast / sovereign / reason / code) that mirrors DSPy's module philosophy. Each lane has its own signature and the router optimizes selection based on cost, latency, and trust requirements.
smolagents (Hugging Face)
- Link: https://github.com/huggingface/smolagents
- Idea borrowed: Minimal, auditable agent loops with code execution as a first-class tool.
- Our implementation: A11oy's tool boundary enforcement (connector firewall + MCP token scoping) mirrors smolagents' philosophy of small, auditable tool surfaces. We extended it with zero-trust credential rotation.
Mastra
- Link: https://github.com/mastraai/mastra
- Idea borrowed: TypeScript-native agentic framework with built-in memory, tracing, and integration connectors.
- Our implementation: A11oy's TypeScript-first architecture and the proof chain API shape are inspired by Mastra's developer experience. Our implementation adds governance gates not present in Mastra.
Frontier Safety & Governance
NIST AI Risk Management Framework (AI RMF)
- Link: https://airc.nist.gov/docs/AI_RMF_Playbook.pdf
- Idea borrowed: GOVERN → MAP → MEASURE → MANAGE lifecycle with organizational accountability for AI risk.
- Our implementation: A11oy's four governance primitives (Constitution, Approval Queue, Proof Ledger, Model Router) directly mirror the RMF quadrant. The Constitution is the GOVERN layer; the Proof Ledger is the MEASURE layer.
MITRE ATLAS
- Link: https://atlas.mitre.org/
- Idea borrowed: A structured adversarial ML threat catalog analogous to ATT&CK but for AI systems. 84 techniques across the ML attack lifecycle.
- Our implementation: A11oy's ATLAS Shield surface maps each of the 84 techniques to a specific defense layer. The Adversary Swarm agents are each loaded with ATLAS technique profiles.
OWASP LLM Top 10 / Agentic Security Initiative
- Link: https://owasp.org/www-project-top-10-for-large-language-model-applications/
- Idea borrowed: Prompt injection, insecure output handling, overreliance, training data poisoning — the canonical risk taxonomy for LLM deployments.
- Our implementation: A11oy's ATLAS Shield OWASP Agentic section maps all 10 risks to specific runtime controls. Agent Zero Trust's MCP token scoping directly addresses OWASP A01 (Prompt Injection) and A06 (Excessive Agency).
AISI Evaluation Methodology (UK AI Safety Institute)
- Link: https://www.gov.uk/government/publications/aisi-approach-to-evaluations
- Idea borrowed: Structured capability evaluations before deployment, with defined threat scenarios and red-teaming protocol.
- Our implementation: A11oy's MirrorEval and the Alignment Review Gate are modeled on AISI's eval methodology — structured dimensions, threshold gates, mandatory re-evaluation on model substitution (Article VI).
Predictive Defense Cortex — Specific Lineage
Swarm-vs-Twin Pattern
- Inspired by: Caldera's adversary emulation architecture (MITRE) + OpenAI Swarm's multi-agent coordination
- Our twist: Runs faster than wall-clock time in a sandboxed digital twin. Governed by Adversarial Covenants (Article IX) throughout. Every swarm step produces a proof packet.
Prediction Ranking Model
- Inspired by: Elastic Detection Rules' severity × confidence scoring + Velociraptor's threat hunt scoring
- Our twist: Likelihood × Impact × Time-to-Exploit triple-factor ranking mapped to both ATT&CK and ATLAS techniques, with forward-looking horizon windows (24h / 72h / 168h).
Pre-emptive Countermove Proposer
- Inspired by: TheHive + Cortex's automated response playbooks + Iron Dome's layered intercept doctrine
- Our twist: Every countermove is a covenant-gated Approval Queue item with the constitutional clause cited. The operator approves or denies from inside Sentra; the decision flows into a11oy's proof ledger.
Adversarial Covenants (Article IX)
- Inspired by: Anthropic's Responsible Scaling Policy's capability thresholds + NIST AI RMF's GOVERN layer
- Our twist: Machine-readable covenant DSL clauses that govern what the swarm can simulate, what countermoves it can stage, and what evidence threshold triggers pre-emptive action. Fully editable, versioned, with diff history.