Building Robust AI Support Agents: Practical Lessons Across the Four Routes
njyx · x · 2026-08-26
A Spec27 blog post discusses practical lessons for building robust AI support agents. The core insight is that robustness work depends on the chosen route, requiring different testing levels for different architectures:
- Agent-first platform: Test observable behaviour.
- Service platform + AI: Test workflow.
- CRM/contact-centre: Test journeys.
- In-house build: Test the full stack.
The article provides specific testing strategies tailored to each route.
Related event: Practical Lessons for Building Robust AI Support Agents(2 posts)→
More from coding & agent
- AgentConnect: Open-Source Alternative to Claude Tag for Multi-Agent Workflows — Div_pradeep · 2026-08-26
- Why Cloud Agents Are the Future: Settling on Conductor and Replicas — jarrodwatts · 2026-08-26
- LangChain open-sources WikiBench to measure how much codebase wikis help coding agents — LangChain · 2026-08-26
- GenOS Multi-Agent Orchestration Framework Seeks Extreme Stress Tests — MonokoEloba · 2026-08-26
- The Downside of AI Coding: Blowing Past the MVP Without Stopping — talkaboutdesign · 2026-08-26
- Perplexity Brain lets agents explore user history on demand — perplexity_ai · 2026-08-26