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 3 of 26
A2A and MCP: the agent protocol stack explained
MCP connects agents to tools. A2A connects agents to other agents. Together they form the full protocol stack for multi-agent CX systems. Here's how they work and why you need both.
Why most AI agents in production are flying blind
57% of organizations have AI agents in production. Only a third are satisfied with their observability. Here's what's wrong with single-call LLM logging and how to build a monitoring stack that actually works.
When vector search fails: GraphRAG for complex CX agent queries
Flat vector search breaks on multi-hop CX queries like 'what discount applies to premium customers in California who signed up before 2024?' GraphRAG uses a knowledge graph to answer these correctly.
Reasoning models for CX agents: when to think slow, when to move fast
Extended thinking models improve decision quality for complex CX scenarios, but add 5-30 seconds of latency and cost 3-5x more. Here's the planning-execution architecture that gets you the benefit without the tradeoff.
Four protocols that power every AI agent
MCP handles tools, A2A handles agent delegation, ACP routes across enterprise frameworks, and ANP crosses org boundaries. Here's how the four protocols divide the work and when each belongs in your stack.
One agent per customer: the CX shift that's here
Most CX agents reconstruct context from a database on every session. The next model gives each customer their own persistent agent instance that carries state between conversations. Here's what that architecture looks like and why it matters.
MCP's 2026 spec changes what your tools can do
The MCP spec RC is out with Extensions, Tasks, MCP Apps, and OAuth hardening. Here's what changes for CX agent builders before the July 28 final.
On-device AI agents for CX: what edge deployment unlocks
On-device AI agents are no longer a research project. Here's what running your CX agent on local hardware actually unlocks in 2026, and what it doesn't.
Building MCP tools your CX agents actually use
Most CX agents have too many poorly-described tools. Here's how to design and build MCP tools that agents call correctly — covering CRM connectors, knowledge retrieval, and action tools.
Context engineering for reliable CX agents
Context engineering is the discipline of deciding what information your AI agent sees, when it sees it, and how it's formatted. Here's how to apply it to production CX systems.
How to debug the agent conversation you can't reproduce
AI agents fail in ways you can't reproduce by resending the same message. Deterministic replay captures what the agent saw and did so you can step through the failure exactly as it happened.
What the agentic CX platform wave is actually building
Adobe, UJET, and NICE all launched agentic CX platforms in H1 2026. They converged on four infrastructure layers. That convergence tells you exactly what your own CX agents need to work in production.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
