Sequoia-backed Edra raises $30M to turn enterprise data into self-improving AI agents. Sequoia-backed Edra raises $30M to build a Living Playbook that turns enterprise data into self-improving, transparent agents. Outcome engineers should treat this as a template for converting messy organizational context into auditable, evolving playbooks so agents act reliably at scale (Principles 06, 03).

Swa launches multi-agent generative AI orchestration solution for enterprise businesses. Swa ships a gateway that orchestrates and compares outputs from multiple models to automate multi-model workflows. This forces architects to design orchestration layers that route tasks, reconcile divergent outputs, and enforce contract-level checks so agents coordinate without breaking downstream systems (Principle 09).

How to Create AI Agents with Neo4j Aura Agent. Neo4j’s Aura Agent connects knowledge graphs to LLM agents for more accurate, explainable, production-ready deployments. If your agents need trustworthy context, adopt graph-backed RAG to improve grounding, provenance and debuggability — a practical Graph-first approach (Principle 11).

Why AI evals are the new necessity for building effective AI agents. The piece argues evaluations must measure interaction-layer trust and user experience, not only model accuracy. Bake continuous UX, safety and outcome-level evals into CI for agents so you catch regressions early and keep human stakeholders aligned (Principle 16).

AI can write your infrastructure code. There’s a reason most teams won’t let it.. Spacelift’s Intent lets LLMs provision cloud resources in real time while applying deterministic OPA guardrails and Spacelift Intelligence to preserve safety and org context. Use this pattern: allow agentic infra actions but gate them with policy-as-code, deterministic checks and audit trails so agents can act without breaking production (Principles 10, 15).