The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
Latest Articles
Testing & EvaluationIs AI Better Than Your Humans? Score Both on One Rubric
Most teams can't say whether AI beats humans because they score them differently. One rubric, run on both, sliced by segment, gives you an honest answer.
Testing & EvaluationEvery Failed Call Is a Test Case You Haven't Written Yet
The gap between staging and production for AI agents is measured in surprise. Here's how to close the loop from live failure to regression gate.
All Articles
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Build Context Editing Into Your Agent and Cut Token Use 84%
Stale tool results pile up in long conversations, raising cost and hurting accuracy. Context editing and the memory tool fix both. Here's how to build them.
How to Test Agents That Call the Right Tool and Still Get It Wrong
Tool-use benchmarks check whether an agent picks the right tool. They miss agents that call correctly, then mishandle the result. Here's how to test for it.
How to Build a Forgetting Policy for Agent Memory
An agent that remembers everything eventually surfaces the wrong fact. Here's how decay, eviction, and supersession keep memory useful instead of just large.
How to Test Tool Argument Correctness in AI Agents
The most common production agent bug isn't picking the wrong tool. It's picking the right tool and passing the wrong arguments. Here's how to catch it.
Your Agent Is Only as Good as Its Harness
Upgrading the model rarely fixes a failing agent. The harness, the code that manages tools, memory, and context, is where agent quality actually lives.
How to Build an Agent That Writes Code Instead of Calling Tools
One JSON tool call per turn is slow, token-heavy, and brittle. A short script that orchestrates tools in a sandbox is faster and cheaper. Here's how it works.
Agent Behavioral Contracts: Stop Drift Before It Hits Callers
Behavioral drift happens with no prompt change and no failing test. Agent behavioral contracts enforce rules at runtime and flag drift before callers do.
Voice Agents That Sense Frustration Before Callers Hang Up
Real-time emotion detection turns acoustic signals into agent decisions mid-call. How the pipeline works, which signals are reliable, and how to wire it up.
Your agent doesn't need more memory. It needs a graph.
Session memory forgets. RAG retrieves but doesn't reason. Context graphs give your CX agent a persistent, queryable model of every customer, issue, and relationship it's ever encountered -- at a fraction of the token cost.
MCP now uses round-trips, not streams, for user input
The July 28 spec deprecated elicitation and sampling. Multi Round-Trip Requests (SEP-2322) replace both with a stateless pattern: the server returns InputRequiredResult, the client collects input, the client re-submits. Here's how to migrate.
How to manage AI agent configs across environments
Your AI agent's system prompt, model settings, and tool definitions are config — and they deserve the same version control, review, and promotion workflow as your code. Here's how to build it.
Why 40% of multi-agent systems fail in production
Nearly half of multi-agent CX pilots fail within six months of production — not because the AI is wrong, but because teams don't understand how multi-agent systems break. Here are the five failure modes and how to diagnose each.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
