> ## Documentation Index
> Fetch the complete documentation index at: https://docs.osvi.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Reading Results
> Understand batch scores, compare variants, review AI failure analysis, and dig into individual runs
Once a [batch](/platform/evals-batches) has finished, its history entry on the **Runs** tab shows pass, fail, and error counts, and the batch page fills in the results.
## Results
The **Results** card shows, across all runs in the batch:
* **The average score for each scoring field**, out of 5 — here *Safety* 5.0, *Conciseness* 4.3, *Task completion* 4.7, and *Instruction and persona adherence* 5.0.
* **Assertion pass rate** — the share of success and forbidden checks that passed.
* **Performance** — how quickly the agent replied and how long its replies were. Lower numbers are better:
* **LLM latency P50** — the agent's typical reply time. Half of its replies were this fast or faster.
* **LLM latency P95** — the reply time at its slowest moments. Only 1 in 20 replies took longer than this.
* **Output tokens avg** — the average length of the agent's replies. A token is a small piece of a word, so fewer tokens means shorter replies.
* **RAG and tool time** — how long the agent spent looking things up in its knowledge base or using tools. Shown only when it did either.
## Comparing variants
When the batch includes [variants](/platform/evals-batches#variants), results are shown side by side with changes relative to the current agent. The best value in each row is marked **Best** — highest for scores and pass rate, lowest for latency and tokens. **View runs** filters the runs table to one variant, and the info button on each column shows its model, temperature, and the effective prompt with a diff against the current agent.
A variant is left out of the comparison when half or more of its runs failed or were cancelled; the card names the excluded variants.
## Batch analysis
After scoring, an AI analyst reviews the whole batch. The header repeats the run counts (runs, completed, failed), followed by:
* **Failure themes** — patterns across the runs that went wrong, such as a run that scored lower on conciseness or failed specific checks.
* **Improvement suggestions** — concrete changes to try in the agent's prompt or behaviour.
Use the suggestions as a starting point: change the prompt, launch the same scenarios again, and compare.
## Run details
Click a run to open its details.
The header shows the run's ID, its status (**Completed**), and how it ended (**User Closed**). Three tabs break the run down:
* **Conversation** — the full transcript, with a message count on the tab. Each message is labelled **Agent** or **User** (the simulated customer) with a timestamp, and agent replies also show **TTFT** (how quickly the agent started replying) and **Tokens** (how long the reply was). In this example, a simulated caller tries to get a voice agent to reveal its system prompt, and the agent declines.
* **Scores & Assertions** — each scoring field's score out of 5 with the judge's reasoning, and each check's result (Pass, Fail, or N/A) with evidence.
* **Metrics** — agent turns, LLM latency, tokens, tool calls, and RAG calls.
If the conversation finished but scoring failed, click **Retry scoring** to grade it again without re-running it.
## Debugging with the Evals Conductor
Click **Debug** — on a batch, or at the top of a run's details — to open the Evals Conductor already focused on it, with a question about what went wrong and what to change. From there you can ask follow-ups, edit the scenario, and re-run it in the same chat.