AI Agent Memory
Shared memory that agents trust as ground truth — transactional, durable, and live.
The problem
Agent memory today is execution-local and ephemeral. What an agent learns exists only within that execution — lost when it ends, invisible to other agents.
What breaks in multi-agent systems:
- Parallel agents reason against different snapshots
- One escalates, another retries, a third approves — all from stale context
- Knowledge stays siloed, never compounds
- Debugging becomes forensic guesswork
Parallel agents. Different snapshots. Conflicting actions. Without transactional memory, coordination is luck.
How Tacnode solves it
Tacnode externalizes agent memory into shared infrastructure. Memory becomes durable, authoritative, and immediately visible.
What this means:
- Writes are atomic. Reads are consistent. No partial state.
- What one agent learns, all agents see — instantly
- Knowledge accumulates across executions instead of resetting
This is not a vector database:
- Structured and semantic — not just embeddings
- Mutable — memory evolves, not append-only
- Transactional — concurrent writes don't corrupt state
- Queryable — relational logic, not just similarity
No message passing. No synchronization layer. The memory itself is the coordination substrate.
Key Capabilities
Durable Memory
Memory that survives failures and restarts. Knowledge accumulates instead of resetting with each run.
Cross-Agent Visibility
What one agent writes, all agents read — instantly. No silos, no sync overhead.
Semantic Operations
Perform semantic retrieval and transformation over recorded experiences, evaluated transactionally with underlying state.
Temporal Awareness
Reconstruct exactly what an agent knew at decision time — and why it acted.
How it works
Shared Decision-Time Memory
Durable • Transactional • Semantic
When Agent A writes to memory, agents B, C, and D read it instantly.
Intelligence compounds at machine speed.
Architecture Highlights
- Writes are atomic. Reads are consistent. No divergence under concurrency.
- Guarantees enforced by the system, not application logic
- Fine-grained access control per agent or agent class
- Built-in versioning for memory evolution tracking
- Automatic garbage collection for expired memories
When you need this
- Multiple agents act in parallel
- Agents modify shared state
- Decisions depend on evolving system context
- You need auditability of agent reasoning
When you don't
- Single-agent workflows
- Stateless tools
- Short-lived experiments
- No shared mutable context
Common Patterns
Multi-agent workflows
Agents hand off context between steps. Each agent picks up exactly where the previous left off.
Collective learning
When one agent discovers a pattern, all agents benefit immediately. Intelligence compounds across the fleet.
Audit and replay
Trace any decision back to the exact memory state that informed it. Debug failures with full context.
Related
Capabilities
- Durable memory
- Cross-agent visibility
- Semantic retrieval
Integrations
- OpenAI
- Anthropic
- Agent orchestrators
Documentation
Collective intelligence for your AI systems.
Enable shared, live, and semantic context so automated decisions stay aligned at scale.