Spec27: Spec-Driven Validation for AI Agents

Safe Intelligence has launched Spec27, a spec-driven validation tool for AI agents. Unlike traditional LLM eval frameworks that score general model behavior, Spec27 lets teams define reusable specifications for the specific mission an agent must fulfill. Tests are generated automatically from those specs and run against the agent's primary interfaces only — no assumption about internal stack, no SDKs or gateways required.

Key Features

Outside-in testing: All tests execute against the agent's exposed API or UI. No need to instrument the agent's internals, which is crucial for agents built on vendor platforms where you don't control the stack.
Spec-driven test generation: Define specs in terms of expected behavior (e.g., “when asked X, must do Y and not Z”). Spec27 auto-generates adversarial and robustness checks, surfacing sensitivities and regressions as models, prompts, or tools change.
Early access: Currently strongest for single-turn agent and application validation. Multi-turn interactions and richer telemetry/tool-call integration are on the roadmap.

Who Is It For

Teams deploying internal agents, vendor agents, or any AI system where reliability matters more than benchmark scores. If you're testing agents on platforms that don't expose internals, Spec27's black-box approach directly addresses that gap.