Observability (2026)

Observability tools show what AI applications and agents are doing in production: tracing each model call and tool use, measuring latency and cost, evaluating output quality, and managing traffic through gateways. Without them, multi-step agent failures are very hard to diagnose.

Check which frameworks and providers a tool instruments out of the box, whether it can be self-hosted, and how it handles sensitive prompt data. Gateways add routing, load balancing and fallbacks across providers; evaluation tools help you catch regressions before users do. For MCP-heavy setups, gateways and inspectors that log tool calls are particularly useful.