An SDK that grades multi-step AI agent task completion using human-blind-spot-aware evaluation, not LLM self-grading
Teams shipping AI agents have no reliable way to know whether an agent actually completed a complex multi-step task. Existing benchmarks are gamed by models that score near-perfect without solving anything real, because LLM judges share the same blind spots as the agents they evaluate. This SDK catches what LLM self-grading misses by combining automated trajectory analysis, deterministic outcome verification, and sampled human grading to produce a calibrated completion score teams can trust.
Demand Breakdown
Social Proof 2 sources
Gap Assessment
4 tools exist (Braintrust, Patronus AI, LangSmith, deepeval (Confident AI)) but gaps remain: Graders are still LLMs that share failure modes with the agent. No judge-blind-spot mitigation, no deterministic outcome verification for verifiable tasks.; Evaluation still relies heavily on LLM judges; no calibrated human-grading sampling to surface and correct judge blind spots at scale..
Features7 agent-ready prompts
Competitive LandscapeFREE
| Product | Does | Missing |
|---|---|---|
| Braintrust | AI observability and eval platform; scores LLM outputs and agent traces with configurable LLM judges. | Graders are still LLMs that share failure modes with the agent. No judge-blind-spot mitigation, no deterministic outcome verification for verifiable tasks. |
| Patronus AI | Agent evaluation suite with trace analysis, adversarial test generation, multi-step benchmarking. | Evaluation still relies heavily on LLM judges; no calibrated human-grading sampling to surface and correct judge blind spots at scale. |
| LangSmith | Tracing, debugging, eval for LangChain agent pipelines with custom evaluators and manual trace review. | No structural defense against judge-model blind spots; manual review does not scale; no automated ground-truth verification for deterministic outcomes. |
| deepeval (Confident AI) | Open-source Python eval framework with 50+ metrics including task completion and tool correctness, pytest-native. | Metrics are LLM-scored by default; no judge-blind-spot sampling or calibrated human loop. |
Leads169BUILDER
Sign in to unlock full access.