01
Prompts change. Quality drifts.
Prompt changes ship unmeasured.
AI AGENT QUALITY PLATFORM
BETTER AGENTS START WITH BETTER EVIDENCE.
Your AI agents.Measured, improved,proven.
Tagnos is the quality platform for AI agents: annotate your data, evaluate your agents, package them as skills, and publish them. Built on the Claude API (the interface used to send requests to Claude), designed for teams without a data science department.
THE QUALITY LOOP01 → 05 → 01
$ tagnos eval --compare v3 v4
✓ Human-calibrated judge ✓ Task completion improved ! Policy regression detected → Review before publishing
FROM HUMAN KNOWLEDGE TO PROVEN AGENTS
Founded by two PhDs in AI and natural language processing · 12+ years of production AI · Built on the Claude API
Built on the Claude API01 / THE PROBLEM
You deployed an agent. But do you know if it’s good?
A convincing answer isn’t always a correct one. Give your team a way to tell the difference.
02
Your labels deserve better.
Manual annotation is slow, costly and not reproducible.
03
Production is a late test.
Errors are discovered in production — by your customers.
04
Knowledge gets left behind.
Every new agent starts from scratch.
02 / THE LOOP
One platform. From data to agents in production.
A continuous quality loop. Each step makes the next one stronger.
- 01
Annotate
Turn expert knowledge into trusted labels.
- 02
Evaluate
Measure your agents against human standards.
- 03
Package
Make validated knowledge reusable.
- 04
Publish
Share skills and plugins; publish agents to the upcoming Agent-to-Agent (A2A) marketplace, where agents can talk to each other.
- 05
Re-measure
Bring new evidence back into the loop.
PUBLISH. LEARN. GO AROUND AGAIN.
03 / THE PLATFORM
Evaluation & benchmarking center
Benchmark prompts, agent versions and agents against each other on your own human-validated ground truth — the examples your experts have checked.
01 / HUMAN + MACHINE
AI does the first pass. You make the final call.
Claude annotates your data first. Your experts validate, correct and turn it into a reliable foundation for better agents.
- Claude-powered first-pass labels
- Expert review and correction
- Validated datasets for evaluation
Annotation reviewSample data
- Intent
- Support request
- AI suggestion
- Needs review
- Human validation
- Approved
02 / SHARED STANDARDS
Many annotators. One definition of quality.
Build a visual annotation schema, bring your experts together and measure where their judgments agree.
- Visual schema builder
- Multi-annotator campaigns
- inter-annotator agreement (how consistently your experts agree)
Campaign / support intentsSample data
- Schema
- Intent · tone · outcome
- Annotators
- Expert A · Expert B
- Agreement
- Compare human labels
03 / EVIDENCE OVER INSTINCT
Evaluation & benchmarking center
Benchmark prompts, agent versions and agents against each other on your own human-validated ground truth. Calibrate an AI model used as a judge on human labels and detect regressions before your next release.
- LLM-as-judge (an AI model scoring answers against human-reviewed examples)
- Prompt versioning and regression tests
- Version-vs-version benchmarks
Evaluation / release comparisonSample data
- Baseline
- Prompt v3
- Candidate
- Prompt v4
- Policy adherence
- Regression flagged
04 / KNOWLEDGE THAT COMPOUNDS
Turn what you’ve learned into what ships next.
Author Claude Skills and Plugins from validated data. Package your team’s know-how as reusable agent capabilities.
- Claude Skills authoring
- Plugin packaging
- Traceability to validated data
Studio / support-triageSample data
- Source
- Validated examples
- Artifact
- Claude Skill
- Version
- Ready for evaluation
05 / AGENTS, CONNECTED
Your next step: connected agents.
The upcoming marketplace will let you publish, discover and connect agents through the Agent-to-Agent protocol. Make proven capabilities available to other teams.
- Publish agents to the marketplace
- Discover reusable capabilities
- Agent-to-Agent (A2A) connections
Marketplace / agent previewSample data
- Agent
- Support triage
- Protocol
- A2A
- Evidence
- Linked evaluation
THE LOOP / PACKAGE & PUBLISH
From validated knowledge to reusable agents
Carry the evidence forward. Skills, plugins and connected agents extend the same quality loop.
Claude Skills & Plugins studio
Turn validated data and evaluation results into tested, documented Claude skills and plugins. Keep reusable capabilities linked to the evidence behind them.
Coming soon
A2A agent marketplace
Publish, discover and connect agents that talk to each other via the Agent-to-Agent protocol. Bring their results back into the quality loop.
04 / PROOF, NOT GUESSWORK
Evaluation & benchmarking center
Benchmark prompts, agent versions and agents against each other on your own human-validated ground truth. A gain in one dimension should never hide a step backwards in another.
PROMPT → DATASET → JUDGE → RESULT
Prompt v3 vs v4
Support agent / release comparison
Prompt v3Prompt v4
pp = percentage points: the difference between two percentages.
Task completion+13 pp
Accuracy+12 pp
Policy adherence−8 pp
Regression detected
Policy adherence dropped. Review the failed cases before publishing.
Illustrative results only. These are not Tagnos performance claims.
05 / BUILT FOR YOUR TEAM
AI quality belongs to everyone.
You don’t need a data science department. You need a clear view of what your agents can do.
Small & mid-size companies
An AI quality manager without hiring.
Bring structure to agent quality with the people and knowledge you already have.
Product teams
Prove each release beats the last.
Make agent quality part of your release process, with clear comparisons and reproducible tests.
Integrators & vendors
Make your know-how reusable.
Package your expertise as skills and agents that other teams can discover and connect.
06 / THE TAGNOS STANDARD
Built for confidence. Grounded in evidence.
Measure before you ship
Evaluate changes against a consistent benchmark before they reach customers.
Human in the loop
Expert judgment sets the standard. AI helps you apply it at scale.
Claude-native
Built on the Claude API, with skills and plugins; Model Context Protocol (MCP) connects AI to tools and data, and Agent-to-Agent (A2A) connects agents to each other.
Full traceability
Follow a result back to its prompt, dataset, human labels and evaluation.
07 / HOW IT WORKS
Your first quality loop starts here.
Start with the agent you have. Build the evidence you need.
- 01
Connect your data or agent
Bring a dataset or test an existing agent through its external interface.
- 02
Annotate with AI + humans
Let Claude suggest labels, then have your experts validate the standard.
- 03
Evaluate & compare versions
Run calibrated evaluations and see exactly where quality changes.
- 04
Deploy as a skill, plugin or agent
Package your knowledge, publish your agent and keep measuring.
agent: support-triage labels: human-validated judge: claude compare: [prompt-v3, prompt-v4] on_regression: review next: re-measure
08 / GOOD QUESTIONS
A little more clarity.
Do I need a data science team?
No. Tagnos is built for product and business teams. AI-assisted annotation, guided evaluation and clear comparisons help your existing experts set the quality standard.
Which models do you use?
Tagnos is built on Anthropic’s Claude API. Claude powers pre-annotation and evaluation, with human labels used to calibrate judgment.
Can you evaluate an agent already in production?
Yes. Black-box external testing lets you evaluate an existing agent with minimal integration, through the interface it already exposes.
Is my data used to train models?
No. Your data is used only to run your annotations and evaluations. Tagnos is built on Anthropic’s commercial Claude API, and we do not use your data to train models. Contact us for our full data processing terms.
YOUR NEXT RELEASE DESERVES EVIDENCE.
Stop guessing. Measure your agents.
Get an introduction to Tagnos. Bring your agent, your questions and your definition of good.
Prefer a conversation?
Tell us what you’re building. We’ll walk through a quality loop together.
LET’S TALK QUALITY
Start with a conversation.
Email us for early access or a product demo. Tell us about your agent and what you want to measure.
Email the Tagnos teamcontact@tagnos.app