AGENTIC AI MOVES FROM CHAT TO PRODUCTION: DATABRICKS LAUNCHES GENIE CODE, MICROSOFT DEBUTS COPILOT COWORK, SALESFORCE SHIPS AGENTFORCE
Enterprise AI agents are graduating from chat to doing real work with guardrails, evaluation, and orchestration across data and business systems. Databricks la...
Enterprise AI agents are graduating from chat to doing real work with guardrails, evaluation, and orchestration across data and business systems.
Databricks launched Genie Code, an autonomous agent for data work that plans, writes, validates, and maintains pipelines and dashboards, and it acquired Quotient AI to embed continuous agent evaluation and reinforcement into Genie products (press release, InfoWorld). Databricks claims Genie Code more than doubled success rates versus leading coding agents on real tasks.
Microsoft introduced Copilot Cowork, which turns goals into background action plans with Work IQ, approvals, and Microsoft 365 governance; it’s in Research Preview with broader rollout via Frontier later this month Redmondmag. Security Copilot added Agentic Secret Finder, and Foundry gained Fireworks AI preview for fast open‑source model inference.
Salesforce launched Agentforce for Contact Center to let autonomous agents resolve voice, chat, SMS, and email cases inside Service Cloud with guardrails and escalation WebProNews. Perplexity also introduced APIs aimed at agentic workflows and orchestration for developers The New Stack.
Vendors are productizing end‑to‑end agent planning, execution, and evaluation, which can cut pipeline toil and speed delivery while keeping audit and controls intact.
Built‑in evaluation and guardrails move agents from demos to dependable services you can monitor, troubleshoot, and continuously improve.
-
terminal
Run a two‑week pilot where an agent builds or fixes a small ETL job or weekly report, gated by approvals and audit logs; track failure modes and recovery time.
-
terminal
Create a policy sandbox: seed bad inputs and permission edge cases to see if evaluation loops flag issues and block unsafe actions.
Legacy codebase integration strategies...
- 01.
Inventory lineage, permissions, and data products in your catalog first; agents need that context to act safely and avoid destructive writes.
- 02.
Start with read‑heavy tasks and idempotent actions; require human checkpoints for state changes until metrics prove reliability.
Fresh architecture paradigms...
- 01.
Design workflows as agent‑driven services with explicit SLOs, approvals, and telemetry from day one.
- 02.
Build an evaluation harness early: log every agent decision, outcome, and policy check to enable RL‑style continuous improvement.