Agent Memory Without Replacing Your Runtime
How engineering teams can integrate persistent memory into existing AI agents via open protocols like MCP, preserving execution boundaries without adopting a heavy runtime platform.
Adding persistent knowledge to an agent does not have to start with replacing its runtime. This guide sets out the storage, retrieval, and execution boundaries to evaluate before connecting an existing agent to a separate knowledge layer.
Key takeaways
- Decoupled knowledge storage: External memory systems like Dnotitia AKB provide dedicated read/write knowledge persistence alongside existing agent orchestration loops (AKB repository).
- Runtime vs. memory boundaries: Active run states, tool retries, and step execution remain the runtime's responsibility, while session decisions and reference documents persist in the knowledge store.
- Hybrid storage architecture: AKB combines Git document revision history, structured metadata, and vector indices for hybrid search across organizational assets (Architecture).
- Flexible protocol transport: Agent clients connect over protocol interfaces or client proxy components depending on client architecture (Works with).
- Core vs. automation separation: Core storage and search mechanisms operate independently from optional external collectors, curation tools, or background workflows.
Execution boundaries: Runtime vs. knowledge layer
Integrating persistent knowledge requires clear operational boundaries between run execution and long-term context. Durable execution state—such as task checkpoints, pending approvals, retry counters, and step sequences—belongs to the agent runtime. A knowledge store provides reusable decisions and reference documents, but it is not a substitute for runtime state recovery, scheduling, or tool execution.
Connecting an agent via standard protocol interfaces does not automatically create reliable memory. Client applications must define explicit read and write policies determining when context is retrieved, updated, or preserved.
| Operational Dimension | Agent Runtime Responsibility | Knowledge Layer Responsibility | Integration Validation |
|---|---|---|---|
| Execution State | Manages active run loops, pending user approvals, retry counts, and step pause/resume. | Persists reusable decisions, project guidelines, and historical session logs. | Verify runtime execution state remains distinct from stored decisions, while reusable knowledge can be retrieved in a fresh session. |
| Context Access | Enforces agent system prompts, tool call parameters, and local memory lifecycle hooks. | Exposes search endpoints, document retrieval, and explicit entity relations. | Validate client policy triggers reads/writes at correct session boundaries. |
| Error Handling | Handles tool call timeouts, execution fallbacks, and agent step retries. | Maintains service availability, search indexing, and data persistence. | Test client behavior when knowledge service times out or returns empty results. |
Storage architecture and data versioning
Production knowledge systems require predictable data modeling and query mechanics. Existing agent runtimes can remain in place, though transport, authentication, tool configurations, and workflow adapters may need adjustment during integration.
Dnotitia AKB manages versioned documents, tabular data, and files alongside explicit entity relationships (AKB architecture):
- Document versioning: Git-backed tracking maintains revision history for text assets and markdown files. This provides transparent revision tracking for user documents, but Git alone is not a whole-system backup or universal rollback tool.
- Storage scope: The documented architecture combines Git document history with PostgreSQL data and metadata and a derived vector index; a Git copy alone is not a complete installation backup.
- Format ingestion: Storing files does not guarantee that every format is parsed or indexed losslessly. Validate extraction and retrieval for the file types used.
Document revision history is distinct from access auditing. The repository’s “Audit log (optional)” section describes MCP-dispatch capture that is off by default, best-effort, and reliant on operator-managed retention. Validate coverage for the interfaces used.
Protocol boundaries and client integration
Exposing persistent knowledge to agent environments relies on open communication standards like the Model Context Protocol (MCP) and dedicated client interfaces (MCP tools selection).
- Transport choices: Clients may use direct Streamable HTTP or the stdio akb-mcp proxy, depending on supported client configuration. The proxy is not required for every client.
- Client compatibility: Documented examples such as Claude Code, Cursor, and custom orchestration loops demonstrate integration paths, but teams should test compatibility for their specific client versions.
- Session lifecycle hooks: The README distinguishes ordinary MCP knowledge tools from dedicated agent-session REST interfaces and lifecycle plugins. Verify support for the selected client and version.
Core storage vs. optional automation
The core AKB engine functions strictly as a read/write knowledge store, providing search, document persistence, and explicit relationship links. The core engine does not ship with an internal knowledge gardener, leaving consolidators, summarizers, and automated maintenance workflows to external integrations.
- Decoupled workflows: External knowledge collectors, curation scripts, and session automation run as separate integrations or services alongside the core engine.
- Ecosystem tools: The independence of the core store does not preclude optional tools (such as knowledge gardeners or ingestion bots) in broader deployments.
- Explicit relations: Linking documents and entities provides structured navigation, but explicit relationships are not a substitute for automated ontology validation or schema reasoning.
Proposed integration pilot
Before deploying persistent memory in production, engineering teams should execute a structured pilot using proposed acceptance tests to verify integration reliability, rather than assuming all behaviors pass automatically:
- Environment setup: Connect the existing agent runtime to a dedicated test knowledge vault.
- Synthetic decision write: Write a synthetic architectural decision record containing explicit source and version fields into the store.
- Fresh session retrieval: Start a clean agent session with no previous prompt history, execute a query, and verify that the agent retrieves and accurately cites the decision.
- Update visibility: Modify the stored decision and measure the interval between a successful write and visibility through direct retrieval and search; inspect returned revisions and cache behavior against an agreed freshness target.
- Access boundary check: Attempt to read or write using an unauthorized identity to confirm that permission boundaries hold.
- Failure resilience: Simulate network latency or service offline status to document defined timeout, error, and fallback behavior in the client environment.
- Write idempotency: Simulate a lost write response, reconcile stored state, and retry using the supported conflict/idempotency mechanism; check for duplicate records and unintended overwrites.
Licensing terms
AKB is released under the Business Source License (BSL) 1.1 (AKB LICENSE).
- Conditional Grant: Production use is permitted under a conditional grant for deployments operating below 100 aggregated Named Seats across related entities and deployments under common control.
- Commercial Licensing Requirement: Deployments with 100 or more aggregated Named Seats, or any deployment offering AKB as a hosted, embedded, or rebranded third-party service at any seat count, require a separate commercial agreement.
- Client Proxy Licensing: The client proxy component is licensed separately under the MIT License (Client Proxy LICENSE).
Engineering terms and deployment scope should be reviewed in the official release license and commercial agreements.
FAQ
Does persistent knowledge replace runtime execution state?
No. The agent runtime remains responsible for active execution loops, task checkpoints, retry counters, and tool approvals. The knowledge layer stores reusable decisions, guidelines, and historical context.
Is the client proxy required for all protocol integrations?
No. Clients can connect directly via protocol endpoints or use the client proxy depending on client support and deployment architecture.
How does document versioning work in AKB?
AKB exposes document revision history. This should not be confused with complete access auditing, a backup of all installation state, or rollback of an agent's execution.
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