How we’re building business observability at Asato

When I joined Asato, what struck me immediately was this: it wasn’t trying to evolve the past—it was starting fresh. No legacy BI stack to unwind. No AI bolted on after the fact. It was a platform born in 2023, shaped by SaaS sprawl, the accelerating adoption of AI across enterprises, real-time questions, and a world where intelligence needs to be native—not optional.

That clarity was rare—and it gave us the freedom to design something fundamentally different. An enterprise observability system that didn’t just monitor but could understand and act.

This post is about how we answered that. And why Business Observability, as we’ve defined it, demands a radically different stack: one that doesn't just analyze, but understands; doesn't just alert, but executes.

A new foundation: Born AI-native, built for execution

Asato isn’t an analytics tool with LLM wrappers. It’s a metadata-native, AI-first system of engagement (yes—not record, for those who know the difference) and action.

We started from the ground up:

• Built for agentic orchestration

• Designed for multi-tenant scale

• Grounded in real-time ingestion, context linking, and trust-aware execution

At the heart of the platform is a simple loop: Link → Think → Execute

It's where Metadata becomes meaning, and meaning drives action.

Modular design, executable by default

From day one, we made every layer pluggable—data stores, agents, orchestration, workflows.

• 100% Kubernetes-native

• Zero Trust (Security/Access Controls) with modern service mesh–fronted networking policy controls

• Multi-tenant safe, version-aware microservices

• Orchestration via LangGraph + Asato MCP/A2A controller layer

Under the hood, we use:

• Document databases for metadata and structured assets

• Vector databases to support semantic lookups and context enrichment

• Graph databases to power lineage, policy paths, and ownership tracing

Everything is designed to compose, isolate, and evolve—so our teams (and yours) can build on it like a platform, not a product.

Ingestion is where context begins

Traditional pipelines treat ingestion like plumbing: move data from A to B, normalize it, and hope it’s usable.

At Asato, ingestion is where semantic intelligence begins:

• Every event is linked to entity graphs

• Every record is time-, identity-, and usage-aware

• We embed ownership, policy lineage, and access intent at the point of collection

This means what enters the platform is not just data. It’s context—structured, queryable, and explainable.

All of this flows into our Asset Knowledge Graph—a living map of entities, relationships, and dependencies that fuels causal inference, letting our platform reason not just about what happened, but why it matters—and what should happen next.

Thinking in layers: Models + agents that understand

We don’t treat AI as a plugin. We treat it as system behavior.

• Our AI/ML stack blends statistical models, semantic enrichment, and LLMs

• We support open-source models and LLM APIs, adapting based on sensitivity and cost-performance

• Models drive: Ranking, Grouping, Forecasting, Anomaly detection, Scenario comparison, and more

But models alone don’t complete the loop. That’s where our agents come in.

Agents that don't just answer — they act

At execution time, we invoke agents orchestrated via LangGraph + MCP/A2A (Agent-to-Agent). These agents:

• Route between intent, metadata, and tools

• Resolve goals into sub-tasks and executable plans

• Respect trust boundaries, permissions, and org-scoped access—inherited from our underlying platform middleware

We don’t just say, “Here’s a spike in SaaS spend.” We say, “Here’s who owns the license, who last used it, why it’s out of policy—and here’s how to fix it.” We don’t just surface “inactive users.” We map them to their asset footprint, identity posture (SSO/MFA), and downstream risks—and let agents triage accordingly.

This is AI with guardrails—intelligent, traceable, and operational.

From analysis to autonomy: The impact of execution

Our core thesis is simple: Insight without execution is noise.

Old tools generate charts. We generate clarity—and let teams act.

Here’s what that looks like:

• Finance can reduce waste without sending 6 Slack threads to IT

• Platform teams can detect ingestion gaps before they become blind spots

• Governance leads can explore lineage, access, and compliance without writing a single query

And everyone—from an engineer to a CIO—can ask real questions and trust that the system knows how to respond.

This is the stack you’d build now—if you could start over

Because we did.

Asato isn’t retrofitted. It’s born in the AI-native, metadata-rich, API-first world:

• Designed post-ChatGPT

• Grounded in schema, access, and usage reality

• Secure by tenant and policy context—not just RBAC

• Executable by default—not optional

We didn’t build another tool. We built an engine that thinks and acts in the shape of the enterprise.

Coming up next

In upcoming posts, we’ll share more detailed insights into Asato’s technical architecture, agent orchestration, and how we govern execution in real-world enterprise environments.
We’ll also highlight early outcome patterns we’re seeing across deployments—from reduced spend blind spots to simplified insight delivery for compliance and governance teams.

• Business Observability in Action: Real-world use cases where Asato connects questions to outcomes

• Designing a Metadata-First SaaS Platform: How structure, not sprawl, drives enterprise clarity

• From Dashboards to Agents: Why BI, as you know it, is over

• Expense 360: Linking spending, usage, and accountability in one unified intelligence loop

Business Observability isn’t just a new dashboard layer. It’s a new way to operate.
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And we’re building it from the ground up.