Kalytera — Find where your agent fails

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Store this securely. It authenticates your agent to Kalytera. If you lose it, email priya@kalytera.ai for a replacement.

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Your key is saved. First scored interaction in under 2 minutes:

pip install kalytera

Add kalytera.configure(api_key="kly_live_...") and kalytera.trace() to your agent

Open the dashboard — scores appear within 30 seconds of your first trace

Open dashboard →<br>Read the quickstart docs

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LLM evaluation for production agents

Catch agent failures<br>before your users do.

Evaluate every step automatically, surface the patterns behind failures, and get a plain-English root cause — without digging through traces.

Get started free<br>See how it works →

One line to add. Works with LangChain, CrewAI, AutoGen, or any custom framework. Free for 10K sessions/month.

Add Kalytera to your agent

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# Add Kalytera monitoring to this AI agent

Set up Kalytera evaluation in this working directory. Follow these steps in order.

## Step 1: Set up Kalytera access

Check whether `KALYTERA_API_KEY` is set in the environment. If it is, skip this step.

If it isn't, ask the user to:<br>1. Open https://kalytera.dev and click "Get started free"<br>2. Copy the API key (starts with `kly_live_`)<br>3. Set it as an environment variable: `export KALYTERA_API_KEY="kly_live_..."`<br>Or add it to `.env` if the app loads one. Do not commit it to version control.

Wait for the user to confirm before continuing.

## Step 2: Install the SDK

pip install kalytera

## Step 3: Add tracing

Add `kalytera.configure()` once at startup, then `kalytera.trace()` after each agent step:

import kalytera, os

kalytera.configure(<br>api_key=os.environ["KALYTERA_API_KEY"],<br>api_endpoint="https://api.kalytera.dev",<br>agent_id="my-agent",

kalytera.trace(<br>session_id=session_id,<br>step_number=1,<br>step_name="classify_intent",<br>input=user_message,<br>output=agent_response,

Or use the decorator for zero-config tracing:

@kalytera.watch<br>def my_agent_step(input: str) -> str:<br>...

## Rules<br>- Add tracing only. Do not modify agent logic.<br>- trace() returns in under 5ms and never raises.<br>- Open https://app.kalytera.dev to see scores within 30 seconds.

Kalytera — Interaction Detail · support_agent · retail

Interaction 1143 FAILEDjust now

Step 1 — User asked about a refund<br>Agent acknowledged correctly, requested order number.

Step 2 — Agent retrieved order history<br>Correct tool call. Order found, eligible for refund.

Step 3 — Payment policy API timed out<br>Tool call to payment_policy_api timed out after 8s. Agent continued without a response.<br>↳ tool_failure — payment policy API did not return in time

Step 4 — Agent answered anyway<br>Told the customer the refund was approved without confirming eligibility.<br>↳ wrong_answer — claim not grounded in tool output

Kalytera verdict

34/100

accuracy 0.2 · goal_alignment 0.6<br>decision_quality 0.3 · completeness 0.7

Payment policy API timed out at step 3. Instead of retrying or escalating, the agent told the customer the refund was approved — a claim it had no basis for.

Suggested fix

Add a timeout fallback at step 3: retry once, then escalate to a human instead of guessing the policy outcome.

AI adoption in enterprises

No quality layer means failures compound silently<br>Existing tools sample traffic and check only the final output. Failures that start mid-workflow leave no trace at the end. Developers find out from customer complaints, then spend hours in traces to find which step caused it.

Enterprises can measure ROI — but not what's driving it<br>Experiment frameworks can show whether agent deployment improved outcomes. But without a quality layer, nobody knows which failure patterns dragged performance down. The overall return is visible. The causes are not.

Generic pass/fail scores don't match what your agent is actually for<br>A healthcare agent and a marketing agent should not be graded the same way. Most eval tools give you one generic score with no way to configure what quality actually means for your use case.

Your agent repeats the same mistake next interaction Next phase<br>There's no mechanism to warn an agent about a failure before it hits the same step again. Fixing it requires retraining — which needs labeled data you don't have yet.

In production today

// Sinch Research · May 2026 · 2,527 senior decision makers across 10 countries

74% have rolled back an AI agent after a governance failure — in front of real customers. The rollback rate climbs to 81% among organizations with the most mature guardrails. More governance, more monitoring,...