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 2 of 20
Structured Outputs: Make Your AI Agent Stop Guessing
JSON mode isn't enough. Learn how constrained decoding, Zod schema validation, and validator-retry patterns cut agent parsing failures in production.
Synthetic Users: Test Your Agent Against AI Personas
Scripted tests catch only the failures you anticipated. Build AI-powered synthetic users that simulate real customers and break your agent before it ships.
How to Build a Trajectory Eval for Your AI Agent
Outcome evals check the final answer. Trajectory evals check the path: tools called, data touched, steps taken. Here's how to build one for a CX agent.
How to Build Production-Safe Credentials for AI Agents
After PocketOS lost its production database to a nine-second AI agent error, here's the credential model that would have stopped it: vaults, scoping, RBAC, and boundary tests.
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.
Learn Agentic AI
Weekly. Patterns for shipping agents that work — MCP, scorecards, regression tests, prompts, model comparisons.
