The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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307 articles · Page 4 of 26
Agent warmup: preload customer context before the first word
Most agents ask for information they already have. Here's how to preload the right customer context before an interaction starts, so your agent sounds like it knows the customer from word one.
What the July 28 MCP spec means for your stateful agents
The July 28 MCP spec is the largest protocol revision since launch. Sessions are removed. Stateless request handling replaces the initialization handshake. Here's what breaks and how to fix it before the deadline.
The OWASP Top 10 for agentic AI: a CX builder's field guide
OWASP published its first Top 10 for Agentic Applications in 2026. Here's what every team building CX agents needs to know, including the three risks that traditional AppSec was never designed to catch.
Extended thinking for CX agents: when slow beats fast
Reasoning models (Claude Opus 4.7, o3, Qwen 3) let your agent think through complex problems before acting. For some CX tasks that's a major improvement. For others, it's a 4-second pause before telling someone their order ships Thursday.
What your agent does when it knows nothing about a customer
The cold-start problem hits every CX agent on first contact: no history, no profile, no context. Here's a practical architecture for handling first-contact customers without making them feel like strangers.
Build evals before you build the agent
Eval-Driven Development (EDD) means your grader ships before your prompt. Here's the complete methodology for building CX agents with evals as the first deliverable, not an afterthought.
Stop Spending Tokens on Agent Routing
Microsoft's open-source Conductor CLI routes multi-agent CX workflows with YAML and Jinja2 -- zero tokens spent on orchestration. Here's when to use it and when to stick with LLM-driven routing.
Fault Injection Is the Missing Layer in Agent Testing
ReliabilityBench found rate limiting is the most damaging fault for AI agents in production -- more damaging than wrong answers. Here's how to inject chaos into your test suite before your users do.
Your CX agent needs an autonomy level before it goes live
Not all CX agents should be equally autonomous. Here's a practical L0-to-L4 framework for deciding how much independence to give your agent for each action type, and what eval thresholds to hit before promoting.
Tool receipts: verifiable proof of what your agent actually did
When your AI agent claims it called a tool and got a result, can you verify that? Tool receipts are structured execution proofs that catch fabricated tool outputs before they damage customer trust.
When your agent spends money: the A2A payments era
The A2A Agent Payments Protocol (AP2) lets AI agents initiate transactions autonomously. Here's what CX builders need to know: the use cases, the controls that make it safe, and how to keep spending observable alongside conversation quality.
Eval sampling for production agents: the 100/10/1 playbook
Running LLM-as-judge on every production conversation costs more than the agent itself. Here's the three-tier sampling system -- 100% lightweight heuristics, 10% LLM judge, 1% human review -- that keeps quality high without multiplying your eval bill.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
