The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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303 articles · Page 8 of 26
How to Build an MCP Apps Widget for Your CX Agent
MCP Apps (SEP-1865) lets your MCP server deliver interactive forms, dashboards, and buttons inside AI chat conversations. Here's how to build one for CX agents.
Pre-Execute Tool Calls to Cut Agent Latency 48%
Sequential tool calls quietly kill your agent's response time. PASTE shows you can pre-execute likely tool calls during LLM thinking time and cut latency 48% without touching your model.
How AG-UI Connects AI Agents to Any Frontend
AG-UI is the open event protocol that connects AI agent backends to any frontend. Here's how it works, why the protocol stack now has three layers, and how to wire it into a real CX agent.
The EU AI Act Deadline Is 11 Weeks Away. Your CX Agent Is Probably High-Risk
The EU AI Act's high-risk compliance deadline is August 2, 2026, just 11 weeks away. Here's what CX teams building AI agents for European markets need to have in place before then.
Cost Per Successful Outcome: The AI Agent Metric Teams Miss
Most teams measure AI agent quality by pass rate. The metric that actually predicts ROI is cost per successful outcome: what each resolution costs paired against whether it actually resolved. Here's how to build it.
MCP Webhooks: Build Event-Driven Agents That React in Real Time
MCP's request-response model breaks when agents need to react to external events. Here's how to build event-driven agents today using stateless HTTP plus webhooks, and what the June 2026 spec will make native.
The Agent Development Lifecycle: Ship, Observe, Improve
Shipping an AI agent is easy. Keeping it reliable after launch is where most teams struggle. The ADLC gives you a structured approach: Intent, Build, Evaluate, Deploy, Observe -- and then do it again.
How MCP Tool Descriptions Break Your Agent
New research shows 97% of MCP tool descriptions have quality issues that hurt agent accuracy. Here's what the smells look like, why they matter, and how to fix them.
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.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
