The agent stack hardens: control, context, and proof

Kontext raises $4M to control what AI agents are allowed to do inside businesses. Kontext enforces security policies on agent actions across business systems. Outcome engineers need explicit permissions and policy enforcement at the tool boundary—Principles 10 and 15.

Teradata aims to make agentic execution of multistep data work more efficient. Tera Harness plans, batches, and prunes agent workflows to reduce tokens, latency, and cost while making execution more predictable. This is Principles 09 and 12 in infrastructure form: orchestration is the product, not a prompt pasted on top of a data pipeline.

Thinking in Systems, Shipping in Loops. AI-native engineering moves human work toward system design, verification loops, constraints, and reusable agent skills. Build the feedback machinery around agents so output quality improves through iteration rather than relying on individual judgment—Principles 09, 14, and 16.

Neo4j Makes the Case for Knowledge Graphs as Shared Context for AI Agents. Neo4j positions knowledge graphs as persistent shared context for enterprise agents, keeping data, rules, and processes aligned. A durable graph gives multi-agent systems a common operating picture instead of fragile, duplicated context—Principles 06 and 11.

Okta Turns Dex AI Agent into a Customer-Zero Proving Ground. Okta uses Dex internally for employee support while testing the identity controls and investments it sells to customers. Customer-zero deployment creates a live evaluation loop for permissions, failure recovery, and business outcomes—Principles 03, 10, and 16.