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#ai-agents

Production AI Agent Architecture: 5 Reusable Patterns from DoorDash & Shippy for Shipping Agents to Production

Most enterprise agents die between the demo and production. Two fresh first-party cases — DoorDash's "Ask DoorDash" assistant and the Allen Institute's Shippy — show the gap isn't which model you picked. It's the five engineering things around the model: deterministic tools, an MCP layer that decouples orchestration from business logic, layered memory, evaluating the agent (not the model), and an FDE-style split between domain and platform teams. Here's the reference architecture, with the numbers.

2026年8月2日9 分で読了

AI Implementation

Why Enterprise AI Agents Fail in Production: 6 Root Causes and the FDE Fix (2026)

Most enterprise AI agents never reach production not because the model isn't smart enough, but because strategy, data, and safety break down first. Gartner expects 40%+ of agentic AI projects cancelled by 2027 and, per Gartner (via FutureAGI), 85% of AI projects never reach production. Here are the 6 root causes—and how forward-deployed engineering fixes each.

2026年7月30日9 分で読了

FDE Insights

OpenAI Presence: Why Model Vendors Are Switching to Forward Deployed Engineers for AI Agents

OpenAI is delivering its new Presence product through Forward Deployed Engineers rather than a self-serve API — a signal that enterprise AI-agent delivery is moving from "sell an API" to "embed on-site." We unpack the shift and give you a four-factor framework for choosing between a model vendor's FDE team and an independent implementation partner.

2026年7月30日9 分で読了

AI Implementation

Enterprise Agent Platform: The 6 Deployment Requirements That Decide Success

When an AI agent goes wrong, it doesn't say the wrong thing—it *does* the wrong thing. That single distinction is why enterprise agent platforms succeed or fail on engineering, not on model IQ. Here are the six deployment requirements—cost, setup, permissions, reliability, security, and continuous evolution—and how 360, GitLab 19.2, and Meta each answer them.

2026年7月30日9 分で読了

AI Implementation

Enterprise AI Adoption in 2026: 88% Are Using It — So Why Do Fewer Than 10% Actually Scale?

In 2026, enterprise AI is wide adoption but narrow production: 88% of organizations use AI, yet fewer than 10% scale an agent in any single function and only 39% see ROI — while a separate 88% of agent pilots never reach production. A data-driven reality check that keeps the two 88%s apart, plus the path into the 12% that actually ship.

2026年7月27日9 分で読了

FDE Insights

x402 Agent Payments Explained: What Enterprises Must Build When AI Agents Pay for Themselves

In one week, AWS and Cloudflare both put the x402 agent-payment protocol into their edge networks — AI agents can now settle in USDC with no account and no API key. The infrastructure layer is now largely built for you. The part still unowned — invoices, VAT, and compliance for machine-to-machine payments — is the part your enterprise has to build.

2026年7月14日8 分で読了

AI Implementation

Why AI Agents Fail in Production (and How Enterprises Ship Them Anyway)

AI agents that dazzle in a demo often break the moment they touch real work. The gap is rarely the model — it's the missing production engineering around it. This guide breaks down the four real reasons enterprise agents fail and the FDE checklist for shipping one anyway.

2026年7月10日7 分で読了

#ai-agents

Production AI Agent Architecture: 5 Reusable Patterns from DoorDash & Shippy for Shipping Agents to Production

Why Enterprise AI Agents Fail in Production: 6 Root Causes and the FDE Fix (2026)

OpenAI Presence: Why Model Vendors Are Switching to Forward Deployed Engineers for AI Agents

Enterprise Agent Platform: The 6 Deployment Requirements That Decide Success

Enterprise AI Adoption in 2026: 88% Are Using It — So Why Do Fewer Than 10% Actually Scale?

x402 Agent Payments Explained: What Enterprises Must Build When AI Agents Pay for Themselves

Why AI Agents Fail in Production (and How Enterprises Ship Them Anyway)

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