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

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235 articles · Page 12 of 20

Illustration of a neural network with low-rank adapter matrices injected between layers, showing only a small percentage of parameters highlighted for training
Learning AI·19 min read

Fine-Tune a 7B Model for $1,500 (Not $50,000)

Full fine-tuning costs $50K in H100s. QLoRA on an RTX 4090 costs $1,500. Learn how LoRA and QLoRA let you train only 0.1-1% of parameters with nearly identical results, with working code for fine-tuning models that understand your agent's tool schemas.

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woman in black long sleeve shirt standing beside woman in gray long sleeve shirt - Photo by Maxime on Unsplash
Operations·12 min read

The AI Agent Dashboard of 2026: What Teams Actually Need to See

Traditional dashboards tell you what went wrong yesterday. The AI agent dashboards teams actually need deliver feedback in the moment, during the call, not after it. Here's what that looks like in practice.

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Three-layer protocol stack diagram showing MCP, A2A, and WebMCP working together for AI agents
Tools & MCP·18 min read

The Three Protocols Every AI Agent Will Speak

The AI agent protocol stack has three layers: MCP for tools, A2A for agent-to-agent communication, and WebMCP for browser interaction. A practitioner's guide to how they work together in production.

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Neural network distillation visualization showing a large teacher model transferring knowledge to a compact student model
Learning AI·16 min read

A 1B Model Just Matched the 70B. Here's How.

How to distill frontier LLMs into small, cheap models that retain 98% accuracy on agent tasks. The teacher-student pattern, NVIDIA's data flywheel, and the Plan-and-Execute architecture that cuts agent costs by 90%.

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Diagram showing interconnected AI agents coordinating a complex customer service workflow
Agent Architecture·14 min read

The Multi-Agent Pattern That Actually Works in Production

Gartner reports a 1,445% surge in multi-agent system inquiries. Here are the orchestration patterns that actually work when real customers call -- and why most teams pick the wrong one.

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a bunch of television screens hanging from the ceiling - Photo by Leif Christoph Gottwald on Unsplash
Operations·12 min read

Stop Reacting to Bad Calls. Catch Problems Before Customers Do

By the time a customer complains, you've already lost. Real-time analytics lets AI agent teams catch failing conversations mid-flight, not in the post-mortem. Here's how to build a proactive monitoring stack that prevents pain instead of documenting it.

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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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