Kalytera – tells you why your AI agent failed, not just that it did

Kalytera — Find where your agent fails

Name or company

Work email

API key

kly_live_...

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Store this securely. It authenticates your agent to Kalytera. If you lose it, email [email protected] for a replacement.

1

pip install kalytera

2

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

3

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

Open dashboard →

LLM evaluation for production agents

Catch agent failures before your users do.

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

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

Paste into Claude Code, Cursor, or Copilot Copy

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:

1. Open https://kalytera.dev and click "Get started free"
2. Copy the API key (starts with kly_live_)
3. Set it as an environment variable: export KALYTERA_API_KEY="kly_live_..."

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

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

Or use the decorator for zero-config tracing:

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

Rules

• Add tracing only. Do not modify agent logic.
• trace() returns in under 5ms and never raises.
• 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

Agent acknowledged correctly, requested order number.

✓

Step 2 — Agent retrieved order history

Correct tool call. Order found, eligible for refund.

✗

Step 3 — Payment policy API timed out

Tool call to payment_policy_api timed out after 8s. Agent continued without a response.

↳ tool_failure — payment policy API did not return in time

✗

Step 4 — Agent answered anyway

Told the customer the refund was approved without confirming eligibility.

↳ wrong_answer — claim not grounded in tool output

Kalytera verdict

34/100

accuracy 0.2 · goal_alignment 0.6 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

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

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

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

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, more investment — and still eight in ten of the most advanced programs had to shut something down.

// Palantir CEO Alex Karp · CNBC Squawk Box · July 1, 2026

Palantir CEO Alex Karp told CNBC on July 1, 2026: "Every single enterprise in this country, these people are LIVID. They are paying for tokens that create no value." Experiment frameworks can measure the overall return. But without a quality layer, nobody knows what failure patterns are dragging that return down — or what to fix to make it better.

// Market Research Pipeline · November 2025

Four agents in production. Two entered an infinite loop. 11 days. $47,000 in API costs. Nobody noticed. The team had monitoring. The loop never appeared in the sample. No loss pattern surfaced. No feedback loop fired. No signal to improve.

62%

Of enterprises already have agents live in production — Sinch 2026

74%

Have rolled back a live agent after a governance failure — in front of real customers

32%

Cite quality as the #1 barrier to production — not cost, not capability

How Kalytera fixes it

AI agents fail differently than normal software. You need a different kind of observability to catch it — one that watches every step, not just the final output.

Existing tools sample 1–10% of traffic. Rare, high-cost failures — infinite loops, cascading tool errors — never appear in the sample. You find out from a $47,000 API bill.

trace everything

100% coverage. Every step of every session.

One line to add. kalytera.trace() is fire-and-forget — under 5ms, never raises, never blocks. Nothing is sampled. If a failure mode exists in your traffic, you will see it.

Session Feed · support_agentlive

1,247 sessions today100% evaluated

✓Session 1249 · classify_intent · 4 steps91/100

✗Session 1248 · billing_dispute · 4 steps34/100

✓Session 1247 · account_lookup · 3 steps94/100

✓Session 1246 · password_reset · 2 steps88/100

✗Session 1244 · payment_update · 5 steps28/100

✓Session 1243 · order_status · 3 steps89/100

Mid-workflow failures produce plausible-looking final outputs. By the time the output is checked, the signal is gone. Developers spend hours in traces trying to find which step caused it.

step-level scoring

Scored at every step, not just the final answer.

Kalytera evaluates each step independently across four dimensions: accuracy, goal alignment, decision quality, and completeness. The failure shows up where it originated — not where it ended up.

Session 1248 · billing_dispute · FAILED

✓Step 1 — classify_intent96/100

✓Step 2 — retrieve_order91/100

✗Step 3 — payment_policy_api12/100

✗Step 4 — generate_response34/100

Failure originated at step 3 — not step 4

A 23% failure rate tells you something is wrong. It doesn't tell you which sessions, which step, or what to change. Most eval tools stop at the score.

root cause

A sentence you can put in a ticket, not a number.

Every failure surfaces with a plain-English root cause: which step, why it failed, how many sessions this week. Not a score — a sentence your team can act on today.

Kalytera verdict · Session 1248

34/100 · FAILED

Root cause

Payment API timed out at step 3. Instead of 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.

One failure is noise. Twenty-three identical failures is a pattern — but only if something groups them. Without automatic detection, recurring issues stay invisible until a customer escalates.

loss patterns

Recurring failures cluster automatically, ranked by impact.

Kalytera runs pattern detection hourly. Failures that share a root cause group into named patterns — ranked by frequency, tagged with first seen, flagged if worsening week over week.

Loss Patterns · billing_agent · last 7d

tool_failure47 sessions ↑ worsening

↳ payment_policy_api timeout at step 3

wrong_answer31 sessions

↳ refund eligibility incorrectly stated

incomplete16 sessions

↳ no confirmation before closing ticket

What you see after 30 days

Most teams connect Kalytera and find failures within the first hour that had been running silently for days. After 30 days the picture gets sharper — not because the tool changed, but because the loss patterns have had time to emerge.

Day 1 — failures you didn't know existed

The first dashboard view typically surfaces 2–4 active loss patterns in agents teams thought were healthy. One-off catastrophic failures that had no monitoring. Silent mid-workflow failures producing plausible-looking outputs.

Day 7 — patterns you can act on

By the end of the first week, recurring failure patterns are named and ranked by frequency. Root cause is surfaced in plain English for each one. The team knows which failure type accounts for 80% of quality issues and has a specific fix to ship.

Day 30 — a feedback loop that runs itself

Every confirmed failure has fed the quality loop. Loss pattern frequency is tracked week over week. Quality scores are improving. The team can show exactly which failure types dropped, by how much, and when.

Stop finding out from your users.

One line to add. Failure patterns surface in 30 seconds. Free for 10,000 sessions per month.

Pricing

Simple, usage-based pricing.

Pay for sessions evaluated. No seat licenses. No surprise bills. Free tier works forever — upgrade when you need more.

Free

$0

/ month, forever

For individual developers and early exploration.

✓10,000 sessions / month

✓All 3 dashboard views

✓Loss pattern detection

✓Plain-English root cause

✓No credit card required

Most teams

Starter

$49

/ month

For teams running agents in production.

✓50,000 sessions / month

✓Everything in Free

✓Custom quality weights

✓Multiple API keys (staging, prod)

✓Team members — unlimited

✓Email support

Growth

$149

/ month

For organizations with multiple agents in production.

✓200,000 sessions / month

✓Everything in Starter

✓Priority support

✓Structured eval export (JSON)

Enterprise — custom contract

200K+ sessions/month. Kubernetes deployment in your VPC. SAML SSO, RBAC, SOC 2, BAA. Dedicated support and SLA. Contact us to get started.

Contact [email protected] →

Research

How AI agents fail in production.

The failure taxonomy and evaluation methodology behind Kalytera are published openly. The methodology is research, not proprietary lock-in.

01

Taxonomy

A Production Quality Taxonomy of AI Agent Failure Modes

Seven failure types optimized for autonomous LLM-as-a-Judge detection, defined by observable signals in traces rather than code root cause.

Available now

02

Methodology

Why Continuous Autonomous Evaluation is Required for Multi-Step Agents

Point-in-time sampling systematically misses failure patterns that only emerge across thousands of interactions over time.

Coming Month 2

03

Methodology

Evaluation Dimensions Optimized for Autonomous LLM Judge Systems

Why goal alignment must be scored separately from accuracy — and what that means for production agent quality.

Coming Month 2

04

Empirical · NET NEW

Loss Patterns in Enterprise AI Agents: A Cross-Industry Analysis

The first cross-industry empirical study of how production agents actually fail, drawn from real evaluation data.

Coming Month 6–9

05

Empirical · NET NEW

The Pareto Distribution of Agent Failures by Intent Type

A small number of intent types

[truncated for AI cost control]