Agentic AI Workflow
Annotation
Agent frameworks are multiplying faster than any standard for judging the data that trains them. We label the whole trajectory — every tool call, every branch, every recovery.
Single input-output pairs don't train agents.
An agent that reaches the right answer through six wasteful tool calls is not the same as one that reaches it in two — but a dataset of prompt-response pairs cannot tell them apart.
Agentic training data has to capture the shape of the decision process: which tool was selected, whether its parameters were right, what the agent remembered from twelve steps earlier, and whether the path it took was efficient or merely correct. That is a fundamentally different annotation task from labeling a sentence or a bounding box.
We built for that shape specifically — including the environment harnesses that generate realistic episodes, rather than only labeling traces a client hands over.
What We Annotate
Seven distinct labeling disciplines, each addressing a failure mode that generic annotation misses entirely.
Environment & sandbox harnesses
Browser, OS, and API-mock sandboxes that generate realistic multi-step episodes for annotators to label — so your training set is not limited to traces you already collected.
Structured trace ingestion
Normalizes logs from LangChain, AutoGPT, CrewAI, or a custom framework into one canonical trajectory schema before annotation begins.
Tool & API schema annotation
Labels whether the correct tool was invoked, with correct parameters, in the correct order — scored against a ground-truth API schema rather than human intuition.
Multi-agent interaction labeling
Scores handoffs, delegation quality, and conflict resolution between cooperating agents — the failure modes that only appear in multi-agent systems.
Long-horizon memory annotation
Labels what the agent should have retrieved or remembered from earlier in a long session, evaluated against a retrieval-correctness rubric.
Efficiency & cost scoring
Flags trajectories that were correct but wasteful — fewest calls, lowest latency, lowest token spend. The operational metric enterprise teams actually budget against.
Session replay & visualization
Annotators watch the agent session play back on a scrubbable timeline rather than reading raw JSON traces — which is what makes step-level judgement reliable at scale.
The same trajectory model powers our robotics work.
A digital agent choosing a tool and a robot arm choosing a grasp are the same shape of problem — state, action, outcome, over time. One schema, one timeline viewer, one export pipeline serves both. If your roadmap crosses from software agents into embodied systems, the annotation standard travels with you.
See robotics annotationTraining an agent? Let's talk about the data.
We're running the agentic annotation pillar with a small group of design partners — teams who want to shape the tooling as it's built, and get priority capacity in return.