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Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.

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157 articles · Page 6 of 14

Layered shield diagram representing defense-in-depth security architecture for AI agents
Security & Compliance·18 min read

Your AI Agent Has No Guardrails

Air Canada honored a refund its chatbot hallucinated. DPD's bot cursed at customers on camera. One e-commerce agent approved $2.3M in unauthorized refunds at 2:47 AM. Here is the five-layer guardrail architecture that prevents all three.

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Watercolor illustration of a shield intercepting data flowing between AI agent tool connections
Security & Compliance·13 min read

Every Tool Is an Injection Surface

Prompt injection moved from chat to tool calls. Anthropic, OpenAI, and Arcjet shipped defenses in the same month. Here's what changed, what works, and what your agent architecture needs now.

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Small chip outperforming a rack of servers
Learning AI·14 min read

Why Your AI Bill Is 30x Too High

Small language models match GPT-3.5 at 2% of the size and 95% less cost. Benchmarks, code, and a migration story from $13K/month to $400.

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Watercolor illustration of descending cost bars alongside token streams flowing through an optimization pipeline
Operations·16 min read read

Your AI Agent Costs $13K/Month. Here's the Fix.

A production customer-service agent burned $13,247 in one month. Prompt caching, model routing, batch processing, and plan-and-execute architecture cut it to $1,100. Real pricing math for every technique.

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Browser window with structured tool definitions flowing between a website and an AI agent
Tools & MCP·13 min read read

Why Browser Agents Waste 89% of Their Tokens

Browser agents burn 1,500-2,000 tokens per screenshot. Chrome 146's navigator.modelContext API lets websites expose structured tools instead, cutting token usage by 89% and raising task accuracy to 98%. Here's how WebMCP works.

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Watercolor illustration of developers at a cafe terrace with MCP diagram on whiteboard — Teal & Copper style
Learning AI·15 min read

Part 1: Claude's 7 Extension Points — The Mental Model

CLAUDE.md, Skills, Hooks, MCP Servers, Connectors, Claude Apps, Plugins — Claude's extension ecosystem is powerful but confusing. Here's the mental model that makes sense of all 7.

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Watercolor illustration of developers at a cafe terrace with LLM layered diagram on whiteboard — Terra Cotta style
Learning AI·17 min read

Part 2: CLAUDE.md, Hooks, and Skills — Three Layers

CLAUDE.md sets conventions. Hooks enforce them. Skills teach workflows. Understanding these three layers — and their reliability spectrum — is the key to a Claude Code setup that actually works.

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Man and woman back to back in office - Photo by Vitaly Gariev on Unsplash
Operations·11 min read

AI Agents Are Great. Until They're Not. When to Put Humans Back in Control

AI agents can handle 80% of your customer interactions with no problem. The other 20% is where your reputation is made or broken. Here's how to design escalation that actually works.

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Watercolor illustration of developers at a cafe terrace with MCP plug-and-socket diagram on whiteboard — Sage & Olive style
Learning AI·17 min read

Part 3: MCP Servers vs. Connectors vs. Apps

All Claude Apps are Connectors. All Connectors are MCP Servers. Understanding this hierarchy — and when to build vs. use managed integrations — saves weeks of unnecessary engineering.

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Watercolor illustration of developers at a cafe terrace with rocket deployment diagram on screen — Dusty Blue style
Learning AI·20 min read

Part 4: All 7 Extension Points in One Production Codebase

50+ skills, multiple MCP servers, scoped rules, safety hooks — here's how all 7 Claude extension points compose in a real NestJS monorepo with 17 projects. What works, what fights, and what we'd do differently.

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Two men filming a scene outdoors with artwork. - Photo by Luke Thornton on Unsplash
Testing & Evaluation·12 min read

Zero-Shot or Zero Chance? How AI Agents Handle Calls They've Never Seen Before

When a customer calls with a request your AI agent has never encountered, what actually happens? We break down the mechanics of zero-shot handling, and how to test for it before it fails in production.

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Code on a computer screen. - Photo by Rob Wingate on Unsplash
Knowledge & Memory·14 min read

Your AI Agent Isn't Learning From Production. Here's What That's Costing You.

Most AI agents are deployed and forgotten. The teams winning with AI have a different strategy: closing the loop from every live call back into the agent itself.

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