The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
All Articles
307 articles · Page 5 of 26
Static API keys don't work for autonomous agents
When you hand an autonomous agent a static API key, you're giving it a skeleton key with no expiry. Here's the per-call permission model that replaces it.
The delegation gap: what you can hand off to your agent
Anthropic's 2026 report found teams use AI in 60% of work but fully delegate only 0-20%. The same gap hits CX teams. Here's a framework for closing it.
Agents that learn while they sleep: async memory consolidation
Post-session async consolidation is the missing layer in most agent memory stacks. Here's how Anthropic's Dreaming primitive works, why Harvey saw a 6x jump, and how to implement the same pattern without Anthropic's API.
Tool result caching: the latency and cost wins hiding in your stack
Your agent re-fetches the same data on every call. Tool result caching cuts latency by up to 70% and inference costs by 40-60% with changes that take days, not weeks. Here's how to classify, implement, and measure it.
AI agents blew their 2026 budget by April. Here's the fix.
One company burned through its entire 2026 AI budget by April. Here's why agent costs run away and how to stop it: per-session token caps, prompt caching for CX, model routing, and circuit breakers.
Your AI agents need their own identity
Most CX agents share a single API key with every other service on your stack. Here's how to implement proper agent identity: OAuth 2.0, scope minimization, JIT provisioning, and instant kill switches.
Parallel tool calls: the 5x speedup in your agent
Sequential tool calls compound latency for no reason. Here's how to fan out independent tool calls in parallel and cut agent response time by up to 5x in production.
Prompt caching: the cost cut most agent teams skip
Prompt caching cuts API costs 41-80% and TTFT by up to 31%. Learn where to put the cache breakpoint, when it backfires, and how to monitor hit rates in production.
Enforcing runtime policies on production CX agents
Guardrails filter what your agent says. Runtime policies constrain what it's allowed to do. Here's why that distinction matters and how to build an enforcement layer your LLM can't override.
Tracing AI agent failures across multi-step tool chains
When a production CX agent returns the wrong answer, the bug rarely lives in the last LLM call. Here's how to trace failures back to their root cause across multi-step tool chains.
Your CX agent hits 95% containment. Why is CSAT tanking?
Containment rate tells you how many conversations your agent handled without escalation. It says nothing about whether they went well. Here's the quality-weighted scorecard that tells the real story.
Design agent tools that won't double-charge on retry
Agents retry when tools fail. Without idempotency, retries create duplicate records, double charges, and corrupted state. Here's the engineering pattern that makes every mutating tool safe to retry.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
