Scott Miller - Super Awesome AI Source (Page 57)

Agentic AI

7 Ways Enterprise Backend Teams Should Redesign Their Agentic Rollback and State Recovery Patterns When Long-Running Multi-Agent Transactions Fail Midway Through Distributed Tool Execution Chains

It starts with a seemingly routine task: an orchestrator agent kicks off a multi-step workflow to provision cloud resources, update a customer record, trigger a billing adjustment, and notify a downstream service. Three tools deep into the execution chain, something breaks. A timeout. A malformed response. A permissions error from

Agentic AI

FAQ: What Enterprise Backend Teams Keep Getting Wrong About Agentic Observability Instrumentation When OpenTelemetry Spans Can't Capture Non-Deterministic Multi-Agent Decision Paths

Here is the uncomfortable truth that most enterprise backend teams are sitting with right now: the observability stack that carried you flawlessly through microservices, Kubernetes, and even early LLM integrations is quietly failing you in the agentic era. You have dashboards. You have traces. You have alerts. And yet, when

LLM orchestration

5 Dangerous Myths Enterprise Backend Teams Believe About Agentic Rate Limit Handling Across Multi-Provider LLM Orchestration Layers

Your agentic pipeline looked bulletproof in staging. Then it hit production, and suddenly a single 429 from one provider cascaded into a full workflow collapse, silent retries ate your token budget, and three downstream agents stalled with zero telemetry to explain why. Sound familiar? As enterprise teams scaled their multi-agent,

Agentic AI

The Six-Week Silent Corruption: How One Enterprise Backend Team Rebuilt Their Agentic Data Lineage Tracking From the Ground Up

It started with a single Slack message from a VP of Finance on a Tuesday morning in late 2025: "Why does our customer churn model show a 14% improvement over the last six weeks when our retention team says nothing has changed operationally?" That one question unraveled a

LLM Architecture

How Enterprise Backend Teams Should Architect Cross-Provider LLM Fallback Chains When Model Version Fragmentation Breaks Multi-Agent Workflows in Production

Picture this: it's 2:17 AM and your on-call engineer gets paged. A critical document-processing pipeline has started returning malformed JSON. The root cause? Anthropic quietly promoted a new default model alias, and the behavioral contract your agent chain depended on shifted underneath you without a single line

Agentic AI

5 Dangerous Myths Enterprise Backend Teams Believe About Agentic Context Window Management (And What They're Really Costing You)

You deployed your multi-agent system. The demos were clean, the stakeholders were impressed, and your team shipped it to production with quiet confidence. Then the invoices arrived. Runaway inference costs. Agents looping indefinitely on stale context. Workflows grinding to a halt at the 128k token boundary. Sound familiar? You are

Agentic AI

7 Predictions for How Enterprise Backend Teams Will Redesign Their Agentic Identity and AuthN Frameworks by Q4 2026

Something quietly broke in enterprise security architecture sometime in late 2025. It did not announce itself with a breach headline or a dramatic zero-day. Instead, it crept in through Jira tickets, Slack integrations, and CI/CD pipelines: the realization that the number of non-human identities operating inside corporate environments had

MCP

MCP Orchestration vs. Direct LLM API Integration: Which Architecture Should Enterprise Backend Teams Choose for Scaling Multi-Agent Workflows in 2026?

There is a moment every enterprise backend team eventually faces: the proof-of-concept agent that ran beautifully in staging starts choking under real production load. Ten concurrent workflows become fifty. Tool calls collide. Context windows bloat. Latency spikes. And the architecture decision that felt "good enough" six months ago

SOC 2 Compliance

7 Ways Enterprise Backend Teams Should Redesign Their Agentic Audit Trail Architecture to Satisfy Emerging SOC 2 Type II Auditor Expectations for Multi-Agent Workflow Accountability in 2026

Something quietly seismic happened to SOC 2 Type II audits in 2026: auditors stopped treating AI agents like software features and started treating them like autonomous actors. That distinction changes everything about how your backend team needs to think about logging, traceability, and accountability. For years, enterprise backend teams designed

Agentic AI

Agentic Memory Stores vs. Traditional Vector Databases: Which Persistence Layer Should Enterprise Backend Teams Choose?

Picture this: your enterprise has deployed a sophisticated multi-agent workflow. One agent researches customer contracts, another drafts proposals, a third cross-references compliance rules, and a fourth orchestrates the whole operation. The pipeline runs beautifully. Then a session ends, a new one begins, and every agent starts from zero. The customer&

secret management

7 Ways Enterprise Backend Teams Should Redesign Their Agentic Secret Management and Vault Integration Patterns for Multi-Agent Workflows

Something quietly seismic is happening inside enterprise backend infrastructure right now. The shift to multi-agent AI workflows is not just a new deployment model; it is a fundamental stress test of every assumption your secrets management strategy was built on. Rotation windows measured in days, static API keys baked into

Agentic AI

7 Predictions for How Enterprise Backend Teams Will Redesign Agentic Cost Attribution and Chargeback Frameworks as Multi-Agent Workloads Scale

Something quietly disruptive is happening inside enterprise finance and engineering departments right now. Multi-agent AI workloads, the kind that spawn sub-agents, call external APIs, consume vector database reads, and chain dozens of LLM inference steps together, are landing on shared cloud infrastructure with no clear owner. And finance teams are