The agent loop is only 1% of the problem. Databricks just announced what they’re doing about the other 99%.

At the Data + AI Summit 2026 (DAIS 2026, June 15–18 in San Francisco), Databricks unveiled a major expansion of Agent Bricks — transforming it from a tool for building AI agents into a comprehensive developer platform for running them in production at enterprise scale.

The numbers behind the announcement are striking: since Agent Bricks launched at last year’s DAIS, over 100,000 agents have been built on the platform, and Databricks is now processing 1+ quadrillion tokens per year in agent workloads. Customers like AstraZeneca, 7-Eleven, Fox Corporation, and Block have shipped production agents on Agent Bricks.

The Missing 99%: Why the Core Agent Loop Isn’t Enough

The Databricks team’s central thesis is worth quoting directly: the rise of agentic coding and more powerful frontier models has unleashed a “Cambrian explosion of agents.” Building the initial agent loop has never been easier. But that core loop is just 1% of the work.

The other 99% is what they call the hidden technical debt of agentic systems:

  • Token capacity management
  • Deployment infrastructure
  • Security and governance
  • Evaluation and monitoring
  • Context management
  • Sharing and reuse

This framing echoes a well-known 2015 Google paper on machine learning systems debt — the idea that the visible ML model is a small island surrounded by a vast ocean of supporting infrastructure. The same pattern is playing out for agents.

The Three Pillars: Choice, Context, Control

Databricks organized the Agent Bricks platform expansion around three challenges:

1. Choice — Model Flexibility at Scale

Agents increasingly need to be composed of many specialized subagents, and different tasks call for different models. A classification step might route to a fast, cheap smaller model; a complex reasoning task needs a frontier model; specialized domain tasks might benefit from fine-tuned models.

Agent Bricks’ Choice pillar addresses model diversity: broad support from frontier proprietary and open-source models down to cheap-but-fast smaller models and custom fine-tunes, all accessible through a unified interface.

The new Omnigent meta-harness sits on top of this — a framework layer that coordinates multi-agent workflows across the model diversity without requiring developers to manually wire each model selection.

2. Context — Getting Data Into Agents

Context is where Databricks has a natural advantage over pure-play agent frameworks. Agents need data, and Databricks has Unity Catalog — one of the most widely deployed enterprise data governance systems.

The DAIS 2026 announcement extends this with:

  • MCP in Unity Catalog — Model Context Protocol integration for external data sources including Google Drive, JIRA, and Slack. Agents can now pull context from these systems through the same standardized MCP interface that’s becoming the industry default.
  • Lakebase — Managed agent memory backed by Databricks’ lakehouse infrastructure. Rather than agents maintaining ephemeral state, Lakebase provides durable, queryable memory that persists across sessions.

For enterprise teams, the Lakebase piece is particularly interesting. Agent memory has been a pain point — most solutions are either too ephemeral (lost between runs) or too rigid (hard to query or audit). Backing it with lakehouse infrastructure means you get durability, queryability, and the ability to audit what agents “remembered” over time.

3. Control — Governance at Enterprise Scale

This is where Databricks is differentiating most sharply from developer-focused agent frameworks. Enterprise deployments need governance, and Agent Bricks now includes:

  • Unity AI Gateway — Centralized governance for agent workloads: policy enforcement, access controls, audit trails. The AI Gateway sits between agents and the systems they access, providing a chokepoint for security and compliance.
  • Databricks Sandboxes — Isolated execution environments for agents. When an agent needs to run code, query databases, or interact with external systems, Sandboxes provide the blast radius containment that enterprise security teams require.
  • LakeWatch — Security monitoring for agent activity. This addresses a real enterprise concern: not just whether agents are running correctly, but whether they’re behaving safely and in accordance with policy.

What This Means for Enterprise Agentic Deployments

The DAIS 2026 announcement is a clear signal about where the agent infrastructure market is heading: toward comprehensive platforms that handle the full lifecycle, not just the exciting parts.

For teams already on Databricks, the Agent Bricks expansion is compelling because it removes the need to stitch together separate tools for model routing, memory, governance, and monitoring. It’s a coherent platform play rather than a collection of features.

For teams evaluating enterprise agent infrastructure, the key questions to ask are:

  1. How does agent memory persist and how can you audit it? Lakebase addresses this.
  2. How do you govern which agents can access which data? Unity AI Gateway addresses this.
  3. What’s the blast radius containment strategy for agent-executed code? Sandboxes address this.

The fact that 100k+ agents are already running on this platform — at AstraZeneca, Block, and Fox Corporation — means this isn’t pre-production vaporware. These are real production deployments, and the platform expansion addresses problems teams have actually encountered at scale.

Retool as Launch Partner

One signal worth noting: Retool announced at DAIS 2026 that they’re a launch partner for the Agent Bricks expansion. Retool’s use case — rapid internal tool building — is an interesting test case for agent bricks, since internal tools are often where enterprise teams deploy their first production agents.

Sources

  1. Databricks Blog — Agent Bricks: Data + AI Summit 2026
  2. Retool Blog — Retool × Databricks at Data + AI Summit 2026

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260617-0800

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