Topic Hub
Build multi-step agent systems that survive real production constraints: reliability, cost, and governance.
Agent runtime and orchestration patterns
Reliability envelopes and human-in-the-loop controls
Cost-aware context and tool-call design
Builder Routes
Federal regulators just rewrote SR 11-7 — and explicitly excluded generative AI and agentic AI from the new framework. Banks are now deploying their most powerful AI systems without a regulatory rulebook, and the internal governance vacuum is the real risk story.
When the two largest model labs simultaneously launched forward-deployed engineering ventures backed by Wall Street capital, they didn't just change how AI gets sold — they changed who owns your production AI architecture. Here's what that means for engineering teams trying to stay in control.
When Microsoft, AWS, Google, ServiceNow, and Okta all ship 'agent registries' within weeks of each other, enterprise architects need to read that convergence carefully — because the agent inventory problem is now a compliance deadline, not a backlog item.
Commonwealth Bank deployed an agentic AI system that doesn't just flag fraud — it generates new detection rules in real-time, then hands them to humans for approval. The 20% fraud loss reduction is real. So is the governance architecture required to make it not a liability.
Most enterprise AI systems fail in production not because the models are wrong, but because nobody defined what 'customer', 'transaction', or 'risk' means consistently across systems. This is a practical implementation guide for building the semantic layer that makes AI grounded, governed, and production-ready.
Pinecone's pivot from vector database to 'knowledge engine' exposes a structural flaw in how enterprise teams built their RAG stacks — and signals a new architecture layer between raw data and agent runtime that will reshape how production AI systems are designed.
Google's BigQuery + Gemini NL2SQL pipeline reveals the core problem every team hits: LLMs generate syntactically valid SQL that is semantically wrong. The fix isn't a better prompt — it's an ontology layer that maps business language to your actual schema. Here's the full architecture, the failure modes, and what to build.
OpenAI's $10B 'Deployment Company' and Anthropic's $1.5B Blackstone-Goldman venture both announced May 4 — and both use the same playbook: embed engineers inside your company, redesign your workflows around their model. Here's what that actually breaks in your AI architecture, governance stack, and vendor strategy.
A production deployment study from Salesforce's Agentforce platform — 722K daily inferences peaking at 1.4M — reveals the three compound AI failure modes that will silently kill your enterprise agent deployment. Here's the architecture that survived.
Microsoft Agent 365 hits general availability today — the enterprise control plane for AI agents. NVIDIA's OpenShell adds the open-source runtime half. Together, they mark the moment enterprise AI governance stopped being a whitepaper and started being a $15/month line item.
