anecticoUnderstand execution
Inspect recorded execution and model usage without losing the customer being served.
AI & agent observability
Understand the run.Follow the customer outcome.
Inspect what your own AI application recorded: model calls, tool steps, usage, cost and quality evidence. Each run stays connected to the customer it served. Anectico records this. It does not run an AI.
Example: an agent session and its proofLLM usage and cost / Agent execution / Quality evidence
- See the execution
Inspect recorded model calls, tool steps and run status.
- Understand usage
Follow tokens, calculated cost and latency with customer context.
- Review quality
Configured evaluations and attributed human feedback stay distinct.
01The execution story
See the steps behind the answer.
Your agent reads a recorded run and follows its model calls, tool activity and declared outcome. You open the run page to check the steps, token use and calculated cost, with the customer context captured with the execution.
- Read the recorded execution sequence and tool steps.
- Inspect model usage, latency and the run’s terminal evidence.
- Follow the customer, request and product activity around the run.
02Quality with provenance
A quality score should have a source.
Versioned evaluators run configured automatic checks. Attributed human feedback records a reviewer’s judgment. Inspect the run and the evidence behind a result before you make a release decision.
- Keep automatic results separate from human judgments.
- Review the evaluator version and subject behind a score.
- Control automatic sampling and budgets.
The workflow, connected
Follow the execution into its evidence and outcome.
Understand the run itself, then inspect the quality and customer context around it.
Capture the run
Record model calls and tool activity, with the customer identity when available.
Inspect the execution
Your agent follows recorded steps, declared status, model usage and calculated cost.
Review the result
Inspect evaluation and feedback evidence, then follow the linked customer story.
The capabilities behind it
Execution, quality and operational control.
Understand the recorded work your agents perform and keep the relevant context and access choices beside it.
Review context and quality
Follow captured context, memory provenance and the sources behind quality judgments.
Govern your agents
Review agent inventory, containment and project-level content policy.
Put it to work
For the questions around your AI application.
Why did the tool step fail?
Inspect the recorded execution and the service or customer evidence around the failed step.
Explore this workflowWhere is this customer’s AI usage going?
Follow recorded model calls, tokens, calculated cost and latency with person-linked context.
Explore this workflowWhat evidence supports this quality result?
Inspect evaluator provenance and keep automatic scores distinct from attributed human feedback.
Explore this workflowYour first useful workflow
Start with one recorded execution.
Verify the captured run before you add automatic evaluations or change content-policy choices.
A few useful answers
A few useful details.
What can I inspect?
Captured model calls, tool steps, execution status, token use, calculated cost, latency and the linked customer evidence, subject to your project’s recorded data and access.
How does quality evaluation work?
Human feedback and configured automatic evaluations stay separate. A score names its evaluator and version. The quality guide explains evaluators, sampling and the evidence behind scores.
Can I give a coding agent access to Anectico too?
Yes. Agents & MCP explains how to connect your own agent so it can investigate and act through Anectico.
Do human feedback and automatic scores mean the same thing?
No. Human feedback records an attributed reviewer’s judgment. Automatic results identify the configured evaluator and its version. They are stored and displayed separately.
Is every run evaluated automatically?
No. Automatic checks need a configured evaluator and a sampling rule. A score always names the evaluator and the version that produced it.
Anectico / Private preview
Bring your AI application.Understand the work it does.
Explore recorded execution and customer-linked quality evidence in private preview.