The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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303 articles · Page 20 of 26
Agentic AI in Production: From Prototype to Reliable Service
Ship agentic AI that doesn't break at 2 AM. Covers orchestration patterns (ReAct, planning loops), error handling, circuit breakers, graceful degradation, observability, and scaling — with TypeScript implementations you can steal.
AI Agent Memory: From Session Context to Long-Term Knowledge
Build AI agent memory systems from scratch in TypeScript. Covers memory types (session, episodic, semantic, procedural), architectures (buffer, summary, vector retrieval), RAG intersection, and privacy-first design.
AI Agent Observability: What to Monitor When Your Agent Goes Live
Build a production observability pipeline for AI agents. Covers latency, token usage, tool success rates, conversation quality, drift detection, structured logging, alerting strategies, and the critical difference between LLM and agent observability.
AI Agent Testing: How to Evaluate Agents Before They Talk to Customers
A practical guide to testing AI agents before production — scenario-based testing with AI personas, scorecard evaluation, regression suites, edge case generation, and CI/CD integration.
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.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
