Scott Miller - Super Awesome AI Source (Page 70)

multi-agent AI

How One Enterprise Platform Team Rebuilt Their Multi-Agent Tool Call Deduplication Architecture After Discovering That Foundation Model Retry Storms Were Triggering Duplicate Billing Events Across Per-Tenant Ledgers

When the platform engineering team at a mid-sized B2B SaaS company called Veridian Systems first rolled out their multi-agent AI platform in late 2025, they were proud of how fast they had moved. Within six weeks, they had onboarded 40 enterprise tenants onto a system that used coordinated AI agents

foundation models

How to Design a Foundation Model Fallback Chain That Maintains Per-Tenant SLA Guarantees When Primary Model Providers Enforce Unexpected Capacity Throttling

It happened to three of the largest AI-native SaaS companies in early 2026 within the same quarter: a primary foundation model provider quietly enforced stricter capacity throttling during peak hours, and suddenly thousands of enterprise tenants started receiving 429 Too Many Requests errors. Support tickets flooded in. SLA breach notifications

AI Development

5 Ways the "Code Review Over Code Writing" Shift Should Force Backend Engineers to Restructure Their AI-Assisted Workflows Right Now

For years, the dominant narrative around AI in software development was simple: AI writes code faster, so developers write more code. Backend engineers adopted tools like GitHub Copilot, Cursor, and a growing roster of agentic coding assistants with a singular goal in mind: accelerate output. But the O'Reilly

AI Engineering

FAQ: Why Per-Tenant AI Agent Cost Attribution Breaks Down When Foundation Models Switch to Output-Based Pricing (And What to Build Instead)

If you're a backend or platform engineer running a multi-tenant SaaS product powered by AI agents, you've probably built some version of a cost attribution pipeline. It tracks which tenant triggered which LLM call, tallies up the tokens, multiplies by a known per-token rate, and writes

EU AI Act

The Compliance Debt Nobody Is Talking About: How Rushed Multi-Agent Deployments Created an EU AI Act Time Bomb

There is a particular kind of dread that settles in when you realize a technical decision you made eighteen months ago was not just a shortcut. It was a liability. Backend engineers across Europe and every company serving European users are beginning to feel exactly that dread right now, as

post-quantum cryptography

Post-Quantum Cryptography Standards vs. Legacy TLS Infrastructure: Which Migration Path Actually Protects Enterprise Backend Systems in 2026?

There is a quiet crisis unfolding inside enterprise data centers right now. It does not announce itself with ransomware alerts or breach notifications. It looks, on the surface, like business as usual. But security architects who understand what is coming know the truth: the encrypted traffic flowing through your TLS-protected

AI Engineering

The Silent Scheduler Problem: Why Backend Engineers Are Discovering That Foundation Model Rate Limits Are Invalidating Their Multi-Tenant AI Agent Priority Queue Assumptions

There is a class of production bug that does not throw an exception, does not trigger an alert, and does not appear in your error logs. It simply degrades, quietly and persistently, until a paying enterprise customer notices that their "high-priority" AI agent has been waiting 40 seconds

AI Security

Shared Tool Execution Environments Are the New Multi-Tenant Security Perimeter: Sandbox Escapes, Dependency Poisoning, and What Backend Engineers Must Fix Now

There is a quiet architectural assumption baked into nearly every AI agent platform deployed today, and it is about to become one of the most exploited attack surfaces of the decade. The assumption goes something like this: if we run each agent's tool calls inside a container, we

AI Agents

A Beginner's Guide to AI Agent Context Windows: Token Limits, Memory Boundaries, and Why Your Multi-Step Workflows Keep Losing Track

You've just built your first AI agent pipeline. It kicks off beautifully: the agent reads a task, plans a series of steps, calls a few tools, and starts executing. But somewhere around step seven or eight, something goes wrong. The agent seems to "forget" an instruction

multi-agent AI

FAQ: Why Enterprise Platform Teams Are Rethinking Multi-Agent Workflow Rollback Strategies in 2026

Agentic AI is no longer a prototype in a sandbox. As of early 2026, enterprise platform teams are running multi-agent workflows that autonomously browse the web, write and execute code, call third-party APIs, mutate database records, send emails, trigger financial transactions, and provision cloud infrastructure. According to Deloitte's

AI Observability

Reactive vs. Proactive AI Agent Observability: Which Monitoring Philosophy Actually Catches Multi-Tenant Workflow Failures Before They Reach the Foundation Model Layer

There is a quiet crisis unfolding inside enterprise AI stacks right now. Multi-tenant agentic workflows are failing in ways that traditional observability tooling was never designed to catch. By the time an alert fires, the damage is already done: a corrupted context window has been handed to your foundation model,

OpenTelemetry

The Observability Illusion: Why Your OpenTelemetry Pipeline Is Structurally Blind to Agentic AI Behavior

Here is a hard truth that most platform engineering teams are not ready to hear: your observability stack is lying to you. Not through bad data, not through misconfigured collectors, and not through careless instrumentation. It is lying to you by design, because the mental model baked into every OpenTelemetry