SMRTR summary
AI agents connected to real systems can fail mid-transaction—charging a card while skipping inventory updates. Patterns like checkpointing, retries, and fallback logic keep agents recoverable without heavy frameworks.
SMRTR provides this summary for quick context. The original article belongs to GitConnected.
Read the original articleThis archive is built from SMRTR newsletter stories. Subscribe for hand-picked stories without the extra noise.
AI agents fail due to API errors and context loss across multi-step operations. Task queues solve this by preserving context, preventing duplicates, managing rate limits, and...
Building reliable AI systems for production requires engineering solutions beyond better prompts to address three critical failure modes. AI systems are probabilistic and...
AI gateways with fallback mechanisms, multi-provider setups, smart routing, automatic retries, timeouts, and canary testing help ensure reliability in LLM-powered applications by...
AI agents waste token budgets through retry loops, inefficient searches, over-powered models, repeated context, and scope creep. Eliminating these inefficiencies frees budget for...
Building an AI agent that works in a demo is easy — building one that runs reliably in production is a completely different challenge. Moving from prototype to production requires...
AI agents differ from chatbots by autonomously controlling workflows through iterative problem-solving rather than single responses, but this complexity increases costs and error...