AI can use digital tools and external systems reliably

Can it execute multi-tool workflows correctly?

Tests whether the system can carry state and intent across multiple connected applications and services.

Cross-system execution is increasingly practical, but brittle handoffs and partial completion still limit reliability.

Can it recover from tool or API failures?

Measures whether the system retries, reroutes, or escalates sensibly after external failures rather than silently stalling.

Recovery patterns exist, but robust failure handling remains one of the main gaps between demos and production-grade autonomy.

Can it learn new tools from documentation?

Tests whether the system can read docs, infer usage patterns, and apply them correctly in a live task.

Documentation-grounded tool use is already one of the better developed areas of practical agent performance.

Can it leave an auditable trail of actions taken?

Measures whether external actions, intermediate states, and decisions can be reconstructed after the fact.

Action logging is increasingly feasible, but trace quality and completeness still vary a lot across agent setups.