Framework Timeline: Evolution & Parallel Research
Comparing the independent development of the NOVA framework with parallel research and industry developments in modular AI memory, agent-first development, and structured context management.
Multi-tier memory architecture with LLM as virtual memory manager, paging information in/out of limited context window.
Even modest ~2k-token contexts cause models to miss facts in the middle. Simply increasing window size is insufficient — retrieval + structure required.
Hierarchical memory organization via segment-level recurrence. 2–57× fewer parameters, 2.5–116× less inference memory vs long-context baselines.
Tree-structured memory for long-form dialogue. Conditional tree traversal instead of linear history or flat vectors.
Light long-range model builds global memory of long context. Memory as learned intermediate structure instead of raw chunks.
Structured, graph-based external memory with explicit edit operations and RL-based memory selection. Deployed in real smartphone assistant.
Open protocol for connecting AI assistants to tools and data sources. LSP-inspired design solving the N×M integration problem.
Multi-agent framework with asynchronous messaging, pluggable memory, and event-driven runtime with learning capabilities.
Practical limits hit with IDE-extended context for AI coding assistants. Even with bigger windows, multi-file workflows don't scale. Begin experimenting with segmented conversation threads (proto-shards).
Built external memory system using modular units called 'shards' — atomic thought-packets with metadata, tagging, and relevance scoring.
Formal whitepaper defining NOVA as a user-centric framework for modular cognitive augmentation through stateless AI systems. Grounded in Extended Mind Thesis, Distributed Cognition, and Working Memory theory.
ADHD-focused productivity assistant using on-device ML with human-in-the-loop design. Parallel thinking about AI as co-regulator inside structured environment.
Zettelkasten-inspired memory: structured notes with attributes, tags, inter-note links. Almost exact parallel to NOVA's shard abstraction.
Recursive problem decomposition with self-verification. Large gains on math tasks without parameter scaling.
First working implementation released as FastAPI server with REST API for shard interaction, creation, semantic search, and listing. Open-sourced with full documentation suite including whitepaper, executive summary, shard memory architecture, and unified consciousness model.
Formal mapping of cognitive functions to shard operations. Defines working memory as active loaded shards, attention as user-selected shards, long-term memory as shard index + embeddings, executive function as user-led shard management, and metacognition as cross-shard synthesis.
Agent-first SDLC: autonomous agents continuously write, test, deploy code. Humans become intent designers and curators.
Rebuilt NOVA as MCP-compliant server with 7 tools and 2 resources, removing OpenAI dependency from the server layer. Cleaner separation: server manages shards, connected LLM handles reasoning.
OpenAI integrates MCP across products. Windows 11 marketed as 'agentic OS' with native MCP support. Protocol transitions from experiment to default integration layer.
Industry analysis of trade-offs between monolithic and modular AI systems. NOVA is explicitly modular cognitive architecture.
AI agent does implementation, docs, tests, versioning. Human 'Editor' sets goals, constraints, acceptance criteria.
Claude Code running as background agents in remote dev environments. Multiple agents working asynchronously, persisting state.
Survey of persistent memory patterns: episodic tables, hierarchical stores, multi-agent shared memory, selective retention.
Three-phase evolution toward agentic AI. Planning, tool use, reflection, collaboration, and memory as core design patterns.
Per-user memory storing prior interactions. Historical context for future coding sessions — persistent, user-scoped memory in production.
MCP donated to Agentic AI Foundation under Linux Foundation. Major spec release introducing Tasks for long-running operations.
Stateful, server-side agent runtime with persistent memory, background execution, and integrated tool calls. Positioned as 'remote operating system' for agents.
Defines Continuum Memory Architecture (CMA): persistent, selectively retained, temporally chained, abstracted memories. Critiques classic RAG as 'stateless lookup'.
Codex App Server as standardized harness. JSON-RPC protocol with items, turns, threads. Persistent agent sessions with shared state.
Every line of code written by Codex agents — ~10× speedup. The crucial ingredient is not the model but the harness. 'Humans steer. Agents execute.'
CodePath redesigns curriculum with Claude and Claude Code as central. AI-assisted development as default baseline.
External, structured, modular memory with orchestration layers is the emerging architectural consensus for AI agent cognition.