Sakana AI's Aquarium Gives Enterprise Agents a Shared Brain for Business Rules
Sakana AI launches Aquarium, an ontology-based agent platform that unifies enterprise data, workflows, and governance for operational AI deployments.
- Sakana AI launched Aquarium, an ontology-based agent platform for enterprise decision support and execution.
- Three pillars: Ontology for semantic data, Managed Agents for workflow runtime, Governance for approvals and audit logs.
- Workflows are defined as YAML with explicit human-approval gates between agent steps.
- Data connects in place from tables, documents, and databases without migration, with live updates.
- Built by Sakana FDEs plus veterans from Palantir, Snowflake, Microsoft, and AWS data platforms.
- Grew out of deployments with MUFG Bank, SMBC Group, and Daiwa Securities; access is consultation-based.
Tokyo-based Sakana AI has introduced Aquarium, an enterprise platform that gives AI agents a shared model of an organization’s data, rules, and approval processes. The launch packages three capabilities Sakana previously assembled during individual customer deployments: a semantic data layer, a managed agent runtime, and governance controls. It moves the research lab into commercial infrastructure for building and operating production agents.
Three layers share one business model
Aquarium organizes enterprise data and agent workflows around three connected layers:
| Layer | Purpose | Key details |
|---|---|---|
| Ontology | Defines business entities, terms, rules, and relationships. | Connects tables, documents, and other sources while records remain in their existing stores. Sakana says source updates flow into the shared model so agents can work from current data. |
| Managed Agents | Runs workflows that combine retrieval, model judgment, function calls, and human review. | Teams describe steps declaratively, and the platform handles workflow deployment and the agent lifecycle. |
| Governance | Controls access and records agent activity. | Administrators can configure permissions and approval gates. Logs capture decision rationales, actions, results, latency, model usage, and configured accuracy metrics. |
The product page represents workflows in YAML, a human-readable configuration format. Each step has a defined role, such as loading data, requesting an agent judgment, calling a function, or pausing for human approval. A single ontology can support multiple workflows, allowing agents to share the same definitions and source connections. That common environment supplies the product’s aquarium metaphor.
Customer deployments exposed repeated work
Sakana built its reputation through research on evolutionary model merging, self-improving systems, and inference-time scaling. Its AB-MCTS research, for example, explores how additional computation during inference can improve model answers. Aquarium applies the company’s work to the operational layer surrounding models.
Sakana says the platform grew from deployments in Japanese financial services. Those projects include an AI lending expert developed with Mitsubishi UFJ Bank, automated proposal generation for SMBC Group, and joint development with Daiwa Securities Group.
Forward Deployed Engineers working with those customers repeatedly had to connect data sources, reconcile terminology, encode decision criteria, evaluate outputs, and improve accuracy. Aquarium turns that project-specific work into reusable infrastructure. Its engineering team includes people with data platform and governance experience at Palantir, Snowflake, Microsoft, and AWS, a background reflected in the ontology-centered architecture.
Semantic gaps become operational risk
Tool-calling frameworks typically give an agent a model, a prompt, and access to functions. Those primitives leave business meaning unresolved. A customer may refer to an account holder in one database, a legal entity in another, and a household in a risk model. Policies may conflict, approval limits may depend on jurisdiction, and identical fields may follow different update schedules.
Aquarium’s ontology provides canonical definitions for those entities and their relationships. Teams can encode policy precedence, ownership, and approval roles once, then reuse them across agents. This reduces the chance that each workflow reconstructs the organization’s vocabulary through prompts and application code.
Keeping source records in existing warehouses and document stores reduces duplicated data and migration work. Integration work remains: teams must map schemas, resolve inconsistent identifiers, define permissions, and decide how agents should respond to missing or stale data. Sakana has not yet published enough technical detail to assess how Aquarium handles freshness guarantees, schema changes, conflicting records, or ontology versioning.
Explicit approval nodes give regulated organizations a control point before an agent triggers a consequential action. The associated logs can support debugging, evaluation, compliance review, and incident investigation by connecting an output to the data, model call, function execution, and approval that produced it.
The closest rivals start from different layers
Palantir Foundry and AIP provide the clearest comparison. Palantir also centers applications on an ontology that connects enterprise data to business objects, actions, and permissions. Aquarium adopts a similar architectural center while drawing its initial customer references from Japanese financial institutions.
LangGraph Platform, Databricks agent tooling, and workflow-as-code systems overlap with Aquarium on orchestration, evaluation, deployment, and governance. Aquarium’s public materials place greater emphasis on a formal semantic layer shared across many agents. Product maturity, connector coverage, deployment options, pricing, and operational tooling remain difficult to compare because Sakana has disclosed few implementation details.
Reusable boundaries for agent systems
- Separate semantics from prompts. Store entity definitions, relationships, and policy rules in a shared layer that multiple agents can reference.
- Give workflow steps explicit types. Distinguishing retrieval, model judgment, function execution, and approval makes control flow easier to inspect and test.
- Place approval before consequential calls. Human review is most effective when it blocks the action itself and records the reviewer’s decision.
- Capture an end-to-end audit trail. Logs should connect source data, model usage, tool calls, approvals, latency, and final results.
The open questions are concrete
Sakana says it is developing continuous updates for ontologies and agents as organizations change, along with AI-assisted tools for constructing agents. Access currently starts through a consultation form, indicating a sales and implementation process led by engineers working closely with each customer.
The public release does not specify pricing, supported connectors, cloud or on-premises deployment options, authentication integrations, APIs, SDKs, rollback behavior, or procedures for testing ontology changes. These details will determine how easily development teams can integrate Aquarium with existing data platforms and delivery pipelines.
Sakana has promised case studies in follow-up posts. Evidence for the ontology-centered approach will depend on measurable results such as task accuracy, human override rates, deployment time, latency, operating cost, and policy violations. Comparisons with simpler retrieval-augmented generation systems, which fetch context and then call tools, would clarify when Aquarium’s additional semantic layer earns its implementation cost.
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