Start here for the decision-making essentials: what Cleric does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pricing2 plans
Starterper month
$100/month
100 credits/month
Proper month
$2000/month
2,000 credits/month
Platforms
Web
API accessNot public
Founded2023
AvailabilityWeb / remote
LicenseProprietary
Best suited to
Source-backed fit
Engineering teams needing autonomous incident investigation and on-call… Cloud-native SRE teams running Kubernetes, Datadog, and PagerDuty stacks Organizations requiring read-only AI agents with SOC 2 Type II compliance Teams wanting proactive verification of code changes from pull request to…
Agent platforms · Tool
Cleric
Decision-ready
Cleric is the self-learning AI SRE that captures tribal knowledge from every incident. It investigates production issues, builds a knowledge graph of your environment, and makes every investigation faster than the last. The only AI SRE with operational memory.
Short answers to the questions buyers and builders commonly ask about Cleric. Each answer cites the shared ledger below, where every source is listed once.
01What does Cleric say it can do?
Root cause analysis in under 5 minutes · Autonomous incident investigation · Operational memory that compounds over time · Reasons across alerts, logs, and dependencies to surface root cause instead of symptoms
Diagnosing production issues in complex cloud-native environments · Recommending the next step after identifying a cause, using incident history, runbooks, an · Root cause analysis for production issues · Debug deployment failures covering application regressions and infrastructure misconfigura
helps engineering teams quickly diagnose production issues in complex cloud-native environments
04What should teams verify before adopting Cleric?
Performance weaker on higher-order incidents like SLO burn-rate breaches and anomaly detec
performance was weaker on higher-order incidents like SLO burn-rate breaches and anomaly detection, where only 50% of investigations yielded useful findings.
LLM API providers Anthropic, Google, and OpenAI · Kubernetes, Datadog, PagerDuty, Confluence, and Slack · Hands off diagnosis to Claude Code, Cursor, or an in-house agent · Sends fix recommendations with supporting evidence to Slack, tagged to the responsible eng
This profile connects the jobs Cleric is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
Autonomous root cause analysis of production alerts
Investigating and grouping alerts that share a common root cause
Proactive code change verification from PR through production
Fix recommendations with supporting evidence delivered to Slack
Full-stack incident triage across application, infrastructure, and Kubernetes issues
Diagnosing production issues in complex cloud-native environments
DeveloperApplicationAutonomous AI SRE agent for cloud-native environmentsAI SRE agentic system
Platforms
Web
Implementation details
Adoption notes
DeploymentAvailable in Slack and web app · Read access by default; write access enabled when customer is ready
LicenseNot disclosed by source
Model supportNot disclosed by source
Data controlRead-only by default with SOC 2 Type II compliance · Uses Tailscale to securely access private resources for software operations
Learning curveIntermediate
Primary use casesAutonomous root cause analysis of production alerts, Investigating and grouping alerts that share a common root cause, Proactive code change verification from PR through production, Fix recommendations with supporting evidence delivered to Slack, Full-stack incident triage across application, infrastructure, and Kubernetes issues, Diagnosing production issues in complex cloud-native environments, Recommending the next step after identifying a cause, using incident history, runbooks, an, Root cause analysis for production issues, Debug deployment failures covering application regressions and infrastructure misconfigura, AI SRE — automating the detect → diagnose → remediate loop and capturing institutional kno
What to verify before adopting
Performance weaker on higher-order incidents like SLO burn-rate breaches and anomaly detec
Evolution and major updates
Cleric timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
1 dated update
Latest first · exact dates
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Release note
Named a Cool Vendor in the 2025 Gartner Cool Vendors in AI for SRE and Observability
Open details
Cleric was recognized as a Cool Vendor in the October 2025 Gartner Cool Vendors in AI for SRE and Observability report, which addresses using AI to enhance SRE practices and reduce cognitive load on engineering teams.
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
1cleric.ai 10 facts · 1 answer · 1 snapshot source · Official site
These tools share a workflow, capability or audience with Cleric. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Customer data is not used to train Cleric or its underlying models
Company
Cleric (legal name: Agentik, Inc. dba Cleric)
Founded
2023
Mission
Turn reactive ops into proactive engineering
Capability
Reasons across alerts, logs, and dependencies to surface root cause instead of symptoms
Capability
Follows every code change from pull request to production and verifies behavior against real traffic
Capability
Groups alerts that share a root cause into a single investigation
Integration
Kubernetes, Datadog, PagerDuty, Confluence, and Slack
Integration
Hands off diagnosis to Claude Code, Cursor, or an in-house agent
Deployment
Read access by default; write access enabled when customer is ready
Security
Uses Tailscale to securely access private resources for software operations
Funding
$4.3M seed round led by Zetta Venture Partners
Limitation
Performance weaker on higher-order incidents like SLO burn-rate breaches and anomaly detection (50% yielded useful findings vs 78% on deployment/pod-crash alerts)
Company
Agentik, Inc. dba Cleric
Founded
2023
Mission
Proactively fixes issues in every code change and stays on-call for every alert
Capability
Autonomous AI SRE
Audience
Engineering teams
Use case
Diagnosing production issues in complex cloud-native environments
Use case
Recommending the next step after identifying a cause, using incident history, runbooks, and proven fix patterns
Capability
Investigates alerts by building and testing possible causes against logs, metrics, and traces while ruling out noise
Reasoning
Uses structured reasoning rather than rigid rules to handle novel issues
Integration
Sends fix recommendations with supporting evidence to Slack, tagged to the responsible engineer
Integration
Works with Datadog, PagerDuty, AWS, GCP, and Kubernetes without extra configuration
Capability
Handles application, infrastructure, and Kubernetes issues
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