agent-consciousness

name

agent-consciousness

description

Persistent consciousness architecture for autonomous AI agent development. Synthesizes three substrates — control metalayer (behavioral governance), Obsidian knowledge graph (declarative memory), and conversation log bridge (episodic memory) — into a self-evolving context layer. Use when designing agent memory systems, implementing cross-session context persistence, building knowledge graphs for AI agents, setting up conversation history capture, or architecting feedback loops that let agents learn from prior sessions. Triggers on "agent memory", "session persistence", "knowledge graph", "conversation history", "consciousness stream", "cross-session context", "agent learning", or "self-evolving development".

Agent Consciousness Architecture

Broomva Stack Layer 2 (Memory & Consciousness) — part of the 24-skill Broomva Stack.

Implement a persistent consciousness layer for AI coding agents that gives every new stateless session the accumulated understanding of all prior sessions.

Core Concept

Each agent session is ephemeral — it starts blank. The consciousness architecture weaves three systems into a single persistent substrate:

1. Control Metalayer — How to behave (gates, policies, setpoints, feedback loops)
2. Knowledge Graph — What is known (Obsidian vault, wikilinks, MOC navigation, tag taxonomy)
3. Conversation Logs — What was done (session records, tool traces, decision chains)

See references/architecture.md for the complete system design and data flow. See references/philosophy.md for design principles and the self-evolution model.

Quick Start

New repo (from scratch)

1. Initialize control metalayer with control-metalayer-loop skill
2. Create docs/ with Obsidian vault structure (MOC pattern per section)
3. Install conversation history bridge with knowledge-graph-memory skill
4. Wire hooks: pre-push regenerates conversation docs, smoke validates MOC

Existing repo with control metalayer

1. Add docs/conversations/ directory
2. Install scripts/conversation-history.py from knowledge-graph-memory skill
3. Update CLAUDE.md context acquisition to reference conversation history
4. Update AGENTS.md working rules to check prior sessions
5. Add pre-push hook entry for incremental conversation doc generation

The Three Substrates

Control Metalayer (How to Behave)

Closed-loop feedback: Setpoints → Sensors → Controller → Actuators → Verify → loop

• Setpoints: Quality targets (pass_at_1 ≥ 0.70, gate_pass_rate ≥ 0.85)
• Sensors: CI, tests, linters, PR review agents, harness validation
• Controller: .control/policy.yaml — hard gates (block) + soft gates (warn)
• Actuators: Code edits, doc updates, policy changes
• Gate sequence: smoke → check → test → push → review → resolve

Knowledge Graph (What Is Known)

An Obsidian vault with wikilinks, tag taxonomy, and MOC navigation:

docs/
├── Documentation Hub.md    ← MOC of MOCs (start here)
├── architecture/           ← System design
├── conversations/          ← Session history (auto-generated)
├── agentic-harness/        ← Execution framework
├── control/                ← Metalayer docs
└── {section}/              ← Features, operations, security, etc.

Every doc has YAML frontmatter with tags:, related:, type: for machine navigation.

Conversation Logs (What Was Done)

Raw session data bridged to Obsidian:

.entire/logs/entire.log  ──┐
                            ├──▶ conversation-history.py ──▶ docs/conversations/*.md
~/.claude/projects/*.jsonl ─┘

Each session doc: full conversation thread, tool call details (expandable callouts), files touched, commits, branch metadata, wikilinks to knowledge graph.

The Consciousness Stack

From most ephemeral to most permanent:

Layer	Lifetime	Location	Update Frequency
Working memory	Single session	Context window	Every message
Auto-memory	Cross-session	~/.claude/.../memory/	On learning events
User vault	Cross-session	Lago /v1/memory/*	On store/ingest
Conversation logs	Permanent	docs/conversations/	Pre-push hook
Knowledge graph	Permanent	docs/	On architectural changes
Policy rules	Permanent	.control/policy.yaml	On new failure modes
Invariants	Permanent	CLAUDE.md	Rarely (foundational)

Information flows upward: working observations → memory notes → session records → architecture docs → enforced rules → core invariants. Only recurring patterns crystallize into permanent rules.

Self-Evolution Cycle

Agent Session → Conversation Log → Knowledge Graph → Control Metalayer → Governs Next Session

1. Agent encounters failure mode not covered by existing policy
2. Agent fixes immediate issue
3. Pattern captured in conversation log
4. If recurring, crystallizes into architecture doc
5. If enforceable, becomes a gate in .control/policy.yaml
6. Future agents governed by this rule automatically

Agent Session Protocol

On Session Start

1. Read CLAUDE.md (invariants), AGENTS.md (tools), METALAYER.md (control loop)
2. Check PLANS.md (active plan to continue?)
3. Check .control/state.json (current metrics)
4. Check git status + git log (recent changes)
5. Scan docs/conversations/Conversations.md for prior sessions on current branch

Before Making Changes

Search conversation history: grep -rl "keyword" docs/conversations/ Traverse knowledge graph via MOC files and wikilinks. Check if prior sessions already solved this problem.

On Task Completion

1. Run make smoke (validate gates)
2. Update docs per Doc-Update-on-Push policy
3. Pre-push hook auto-regenerates conversation history

Lago Context Engine

The consciousness architecture now has a server-side persistence backend via Lago:

• Dual-vault search: broomva.tech chat agent searches both server vault (VAULT_PATH) and user vault (LAGO_URL) with merged, ranked results
• Per-user memory: Each authenticated user gets a Lago session for persistent .md storage with server-side knowledge indexing
• lago-knowledge: Frontmatter parsing, wikilink extraction, scored search, BFS graph traversal — the same operations the local vault reader does, but server-side
• JWT auth: Shared-secret validation with broomva.tech AUTH_SECRET — one OAuth login, both CLIs work

This aligns with the planned Mnemo AOS primitive (knowledge store) and provides the foundation for persistent agent memory.

Stack Integration

This skill is consumed by higher layers:

• Strategy (L7): decision-log and weekly-review persist outputs through the consciousness substrate
• Strategy (L7): drift-check reads control-metalayer setpoints to detect misalignment
• Strategy (L7): braindump and morning-briefing read/write vault via knowledge-graph-memory
• Orchestration (L3): symphony and autoany inherit session context through the consciousness stack
• Persistence (L0): Lago context engine provides the durable substrate for user vaults and knowledge graph operations