Laying the Foundations
A deep dive into Gaia 2.0, the first stable release of the platform, focusing on its core building blocks and the problems it set out to solve.
Gaia 2.0 — Laying the Foundations of an Enterprise AI Platform
Yesterday we released Gaia 2.0, the first stable version of the Gaia platform.
This release marks a clear transition:
from experimental prototypes and internal tooling → to a coherent, production-ready foundation for building, operating, and evolving AI-powered systems inside real organizations.
Rather than chasing surface-level features, Gaia 2.0 focuses on something more fundamental: structure.
What Gaia 2.0 Is About
Gaia 2.0 introduces the core primitives that everything else in the platform will build upon:
- Projects to define boundaries and ownership
- Agents as modular AI execution units
- Conversations as persistent, stateful interaction threads
- Data ingestion as a first-class concern
- Evaluation scaffolding to start measuring outcomes
Together, these form the minimal but essential skeleton of an AI lifecycle platform — one that treats AI systems as long-lived, governable assets, not disposable demos.
Projects — Defining Scope, Ownership, and Boundaries
What shipped
Projects are the top-level organizational unit in Gaia.
They group together agents, conversations, data sources, and evaluations under a single logical boundary.
Why this matters
In real environments, AI systems don’t exist in isolation:
- Different teams own different use cases
- Data access must be scoped
- Responsibility and accountability must be explicit
Projects provide the first layer of structure needed to support this reality.
How it’s used
A single organization can now:
- Separate internal copilots from customer-facing assistants
- Isolate experimental work from production workloads
- Assign ownership at the project level instead of per prompt or per agent
This is a small abstraction with large implications for governance and scale.
Agents — Encapsulating AI Behavior
What shipped
Agents in Gaia 2.0 represent encapsulated AI actors with:
- Defined configuration
- Clear responsibilities
- The ability to be invoked repeatedly across conversations
Why this matters
Most AI tools blur the line between:
- a prompt,
- a conversation,
- and a piece of business logic.
Agents introduce separation.
They allow teams to treat AI behavior as:
- something reusable,
- something inspectable,
- and eventually, something measurable.
How it’s used
Instead of rewriting prompts or logic for every interaction, teams can:
- define an agent once,
- reuse it across many conversations,
- and reason about its behavior independently of any single user session.
Conversations — Persistent Context, Not Disposable Chats
What shipped
Conversations in Gaia 2.0 are stateful and persistent.
They capture:
- user inputs,
- agent responses,
- and the evolving context of an interaction over time.
Why this matters
Enterprise workflows are rarely single-turn:
- Questions evolve
- Context accumulates
- Decisions depend on previous steps
Gaia treats conversations as long-lived interaction threads, not transient chat windows.
How it’s used
A conversation can now:
- span multiple user requests,
- invoke the same agent multiple times,
- and retain context without manual prompt reconstruction.
This unlocks more realistic, task-oriented AI interactions.
Data Ingestion — Bringing Real Information Into the Loop
What shipped
Gaia 2.0 introduces early support for ingesting data into the platform, enabling agents to operate on information beyond the immediate prompt.
Why this matters
AI systems are only as useful as the context they can access.
By treating data ingestion as a platform concern — not an afterthought — Gaia starts closing the gap between AI reasoning and real organizational knowledge.
How it’s used
Teams can begin experimenting with:
- document-backed agents,
- knowledge-aware conversations,
- and early forms of retrieval-augmented workflows.
This is the first step toward grounding AI behavior in real data.
Evaluation — Measuring Instead of Guessing
What shipped
Gaia 2.0 includes the initial scaffolding for evaluating AI outputs.
While intentionally lightweight, this establishes an important principle:
AI behavior should be observable and comparable — not assumed.
Why this matters
Without evaluation:
- improvement is guesswork,
- regressions go unnoticed,
- and trust is hard to earn.
Even basic evaluation mechanisms create the conditions for responsible iteration.
What This Release Enables
Gaia 2.0 is not about feature density.
It’s about platform integrity.
With this release, teams can begin to:
- structure AI work instead of improvising it,
- reuse behavior instead of duplicating prompts,
- and think in terms of systems rather than chats.
It is intentionally opinionated in its foundations — because those foundations will determine everything that comes next.
Looking Ahead
With the core building blocks now in place, we’re starting to explore how they can be composed, extended, and connected in more powerful ways.
Expect future iterations to focus on:
- richer orchestration between agents,
- clearer visibility into behavior and outcomes,
- and tighter alignment between AI capabilities and organizational workflows.
For now, Gaia 2.0 sets the stage.
And that’s exactly what it was meant to do.