The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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235 articles · Page 3 of 20
AWS just gave your agent 15,000 cloud tools
The AWS MCP Server is now GA. One tool call reaches any of 15,000+ AWS APIs, sandboxed Python execution lets agents run multi-step operations, and Agent Skills replace heavyweight SOPs with on-demand guidance. Here's what changed and how to wire it.
Your agent re-reads its own manual on every call
Datadog's 2026 State of AI Engineering report found that 69% of input tokens go to system prompts, yet only 28% of LLM calls use prompt caching. Here's how to diagnose the problem and fix it without rewriting your agent.
MCP Apps: Build UIs That Render Inside AI Chat
MCP Apps let your tools return interactive HTML dashboards, forms, and visualizations that render inline in Claude, ChatGPT, and VS Code. Here's how to build them for CX agents.
Trajectory Eval: Catch Agent Bugs Output Scoring Misses
Final-output scoring misses 20-40% of agent regressions. Trajectory evaluation scores every step an agent takes -- tool calls, reasoning decisions, order of operations -- and catches the bugs that output-only evals can't see.
Shadow Mode: Deploy AI Agent Updates Without Risk
Shadow mode runs your new agent version in parallel with production, comparing behavior before customers ever see it. Here's how to build the full deployment pipeline from shadow to canary to 100%.
Your CX Agent Crashes Mid-Task. Here's the Fix.
When your CX agent crashes mid-refund or mid-booking, the customer is stuck. Durable execution guarantees long-running agent tasks survive failures. Here's how to build it.
AG-UI: The Protocol That Connects Agents to UIs
AG-UI is the open event-based protocol that streams AI agent state to any frontend in real time. Here's how it works, what events it defines, and how to wire it up in TypeScript.
Your Agent Is Already a State Machine. Make It Explicit.
Every production AI agent is secretly a state machine. Making it explicit gives you checkpointing, testable paths, and observable state transitions -- without rewriting your agent logic.
Your Agent Has Observability. It Doesn't Have Measurement.
89% of AI teams added observability. 52% added evals. But only 31% can say whether their agent is getting better or worse. Here's the difference between watching your agent and actually measuring it.
Why CX Agents Fail Between Conversations
Your AI agent handles the call perfectly and still fails your customer. The problem isn't the conversation -- it's everything that happens after it. Here's how async task queues fix the gap.
AI Agent KPIs: What to Measure Before You Ship
Only 31% of teams have a measurement framework for their AI agents. Here's how to define task completion rate, escalation rate, cost per outcome, and CSAT delta before your first production interaction.
MCP Auth in Production: Scopes, Tokens, and Tenant Isolation
Most MCP servers ship with no auth. Here's how to add OAuth 2.0 scopes, per-tenant tool sets, and client isolation before your MCP server becomes load-bearing production infrastructure.
Learn Agentic AI
Weekly. Patterns for shipping agents that work — MCP, scorecards, regression tests, prompts, model comparisons.
