COMPUTER + DATABRICKS · BETTER TOGETHER
Your Agent Bricks agents know the lakehouse. Computer helps them know the enterprise.
Agent Bricks reasons over your lakehouse. But the context that explains a customer lives in 30+ systems that never landed in Delta — and memory scoped to one agent can't be shared. Computer gives every agent one live, cited view of the whole enterprise.
We don't replace Databricks. We make Agent Bricks production-grade.
THE WHY
Why your Agent Bricks agents start cold
Strong on what's in the lakehouse, blind to the rest. The systems that explain an account never landed in Delta, memory scoped to one agent can't be shared, and retrieval gets harder as the store grows. The result: partial answers, and pilots that stall before production.
01
Memory stops at the lakehouse
Agents reason only over Delta. The systems that explain an account — Salesforce, ServiceNow, Jira, Slack — sit outside.
Context lives elsewhere
02
Isolated, not shared
Memory is scoped to one agent. Deploy a dozen and none of them learn from each other.
No shared context
03
Retrieval degrades at scale
Answer quality erodes as memory grows — the store gets bigger, retrieval gets harder.
Quality erodes as it grows
COMPARE
Agent Bricks alone vs.
Agent Bricks + Computer
Ask "why is the Acme renewal at risk?" Alone, Agent Bricks returns what's in the lakehouse. With Computer, it returns the whole picture — 3 open P1 cases in ServiceNow, a defect owned by engineering in Jira, a champion who's moved on in Salesforce — cited to source, with next steps staged for approval.
Capability | Agent Bricks alone | Agent Bricks + Computer |
|---|---|---|
Memory scope | Memory stops at the lakehouse. The context that explains an account lives outside Delta. | One view of the enterprise. Lakehouse data correlated with the 30+ systems around it — cited to source. |
Sharing | Isolated per-agent. Nothing one agent learns is shared with the next. | Shared across agents. One knowledge graph, the same context for every agent. |
Retrieval at scale | Retrieval degrades at scale. Answer quality erodes as memory grows. | Quality holds at scale. Hybrid retrieval keeps accuracy steady as memory grows. |
WHAT YOU GET
The whole enterprise, built in
One connected view
Not one lakehouse at a time. AirSync pulls in the 30+ systems that don't live in Delta and correlates them into one knowledge graph — cited to source.
Precision
Answers you can trust. Hybrid retrieval traces every answer to its source record. Cited answers, not hallucinations.
Efficiency
Lower cost per answer. Pre-correlated memory means one tight retrieval, not a fan-out. Cost per query stays predictable at scale.
Safety
Your CISO will sign off. Every write is staged and reversible. Access mirrors your Unity Catalog governance, with a full audit trail.
Shared across agents
Not isolated per-agent. One knowledge graph gives every agent the same governed context — so they learn together instead of starting cold.
ENTERPRISE-BENCH
Accuracy you can trust, cost you can forecast
Your agents have to hold up as your data grows — 500 records or 50,000. On Enterprise-Bench, the open, vendor-neutral benchmark for enterprise AI agents, pre-correlated memory beat query-time retrieval on the same data and model — and held as the data scaled 256×:
94.3%
Accuracy vs 63.6% baseline
4.4×
Fewer tokens per correct answer
Flat
Cost at scale vs +29%
256×
Data scaling, store still holds
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