Scott Miller - Super Awesome AI Source (Page 59)

EU AI Act

How One Enterprise Backend Team Discovered Their Multi-Agent Workflow Was Silently Violating EU AI Act Transparency Obligations Mid-Deployment , And the Runtime Governance Layer They Built in 72 Hours to Avoid a Forced Shutdown

It started with a routine internal audit. Not a dramatic whistleblower email, not a regulator knocking on the door. Just a junior compliance analyst at a mid-sized European fintech, cross-referencing their newly deployed loan-decisioning pipeline against the EU AI Act's updated enforcement obligations that came into full effect

Multi-Agent Systems

How to Build a Runtime Agent Dependency Graph for Enterprise Backend Teams: Detect and Resolve Cascading Version Conflicts Before They Break Multi-Agent Workflows in Production

You deployed six AI agents last quarter. They share three tool libraries. Everything passed CI. Everything passed staging. Then, on a Tuesday morning in production, Agent 4 silently starts returning malformed outputs. By the time your on-call engineer traces the root cause, Agent 2 had upgraded its internal version of

Agentic AI

5 Dangerous Myths Enterprise Backend Teams Believe About Agentic Token Budget Management That Are Silently Inflating Cloud Bills and Degrading Multi-Agent Reasoning Quality at Scale

Something quietly alarming is happening inside enterprise AI infrastructure teams in 2026. Agentic workloads have exploded. Multi-agent pipelines now orchestrate everything from customer support resolution to autonomous code review, financial compliance checks, and supply chain reasoning. And yet, the discipline of token budget management inside these systems remains shockingly immature.

Agentic AI

Agentic Rollback and Recovery: How Enterprise Backend Teams Should Architect Resilience When Multi-Agent Workflows Leave Irreversible Side Effects

Picture this: a multi-agent workflow fires off to handle a customer order fulfillment. One agent charges the customer's card via Stripe. A second agent reserves inventory in your warehouse management system. A third agent dispatches a shipping label through FedEx's API. Then the fourth agent, responsible

AI Agents

Deterministic Orchestration vs. LLM-Native Planning Loops: The Enterprise Agent Architecture Decision You Can't Afford to Get Wrong

Somewhere in your organization right now, a backend team is debating how to wire up its next AI agent system. One engineer is drawing DAGs on a whiteboard. Another is arguing that the LLM should just figure out the steps at runtime. Both are right. Both are dangerously incomplete. And

AI Agents

Agent-Native Message Queues vs. Kafka: Which Backbone Should Enterprise Backend Teams Choose for Agentic Workloads?

There is a quiet architectural war being waged inside enterprise backend teams right now. On one side: Apache Kafka and its battle-tested cousins, the traditional event brokers that have reliably shuttled billions of events per second for over a decade. On the other: a new class of infrastructure purpose-built for

AI Agents

How to Build a Cross-Organizational Agent Identity and Credential Rotation System for Enterprise Backend Teams Managing Multi-Agent Workflows Across Third-Party API Boundaries

The rise of autonomous, multi-agent AI systems has quietly introduced one of the most underappreciated security challenges in modern enterprise engineering: who is the agent, and does it still have the right credentials to act on your behalf? As of 2026, most enterprise backend teams have deployed at least some

AI architecture

The Agentic Memory Stack: How Enterprise Backend Teams Should Architect Persistent Memory Layers Without Corrupting Agent Decision State

There is a quiet crisis unfolding inside enterprise AI teams right now. The agents are getting smarter, the context windows are getting longer, and the vector stores are filling up fast. But somewhere between a short-term scratchpad and a long-term retrieval call, something goes wrong: the agent starts making decisions

AI FinOps

7 Predictions for How Enterprise Backend Teams Will Redesign Their Agentic Cost Attribution Models by Q4 2026

Something quietly alarming is happening inside enterprise engineering organizations right now. AI agents are shipping to production at a pace that far outstrips the financial infrastructure built to track them. Token bills are ballooning. Tool-call invocations are multiplying across orchestration layers. And the FinOps dashboards that worked perfectly well for

Multi-Agent Systems

Push-Based Agentic State Sync vs. Pull-Based Polling: Which Architecture Should Enterprise Backend Teams Choose for Distributed Multi-Agent Systems?

Imagine you have thirty AI agents running simultaneously across data centers in Frankfurt, Singapore, and São Paulo. Each agent is mid-task, reading shared state, writing decisions, and coordinating handoffs with sibling agents. Now ask yourself: how does every one of those agents know what every other one is doing, right

AI Agents

How to Build a Formal Agent Handoff Protocol for Enterprise Backend Teams

Long-running agentic tasks are no longer a research curiosity. In 2026, enterprise backend teams are deploying autonomous AI agents that orchestrate multi-step workflows spanning hours, days, or even weeks. These agents book resources, process claims, run compliance checks, and coordinate across a dozen internal services before a single business outcome

Agentic AI

Enterprise Backend Teams Are About to Make the Same Mistake With Agentic Consensus Protocols That They Made With Distributed Database Transactions in 2019

There is a particular kind of organizational amnesia that strikes engineering teams every time a genuinely new paradigm arrives. The details change. The hype changes. The stack changes. But the mistake stays remarkably consistent: teams reach for the new tool with the same mental model they used for the old