The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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215 articles · Page 13 of 18
AI Agent Tools: MCP, OpenAPI, and Tool Management That Actually Scales
How production AI agents discover, execute, and manage tools — from MCP protocol to OpenAPI auto-importing, security sandboxing, and multi-tenant tool infrastructure.
Build your own AI agent memory system — what breaks when real users show up?
Build a complete memory system for customer-facing AI agents — session context, persistent recall, semantic search. Then learn what breaks when real customers start returning.
Build your own AI agent tool system — what breaks when you add the 20th tool?
Build a complete tool system for customer-facing AI agents from scratch — registry, execution, auth, monitoring. Then learn what breaks when real customers start calling.
Call Logs Aren't Just Records. They're Your Best Product Feedback Loop
Most teams treat call logs as a compliance archive. The teams winning with AI agents treat them as a real-time signal about what's working, what's breaking, and what customers actually want.
MCP Deep Dive: Advanced Patterns for Agent Tool Integration
Production MCP patterns for teams who've built their first server and need to scale it — OAuth 2.1 with PKCE, Streamable HTTP transport, gateways, sampling, dynamic tool registration, and multi-tenant security.
Multimodal AI Agents: Voice, Vision, and Text in Production
How to architect multimodal AI agents that process voice, vision, and text simultaneously — from STT→LLM→TTS pipelines to vision integration, latency budgets, and production fusion strategies.
Voice Agent Platform Architecture: The Stack Behind Sub-300ms Responses
Deep dive into voice agent architecture — the STT→LLM→TTS pipeline, latency budgets, interruption handling, WebRTC vs WebSocket transport, and what orchestration platforms leave on the table.
Fine-tuning vs RAG: why most teams pick wrong and how to decide
When to fine-tune, when to use RAG, and when you need both — with hands-on LoRA fine-tuning and RAG implementation on the same task to show the difference.
Multi-Agent AI Systems: Build an Agent Orchestrator Without a Framework
Build a multi-agent system from scratch — delegation, planning loops, and inter-agent communication — before reaching for LangGraph or CrewAI.
Streaming AI Responses: SSE, WebSockets, and the Architecture Behind ChatGPT's Typing Effect
Build three streaming implementations from scratch — SSE, WebSocket, and HTTP/2 — and learn why token-by-token rendering is harder than it looks.
Who's Testing Your AI Agent Before It Talks to Customers?
Traditional QA validates deterministic code. AI agent QA must validate probabilistic conversations. Here's why that gap is breaking production deployments.
How to Evaluate AI Agents: Build an Eval Framework from Scratch
Build a working AI agent eval framework in TypeScript and Python. Covers LLM-as-judge, rubric scoring, regression testing, and CI integration.
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