# Cleric: briefing for AI agents

> Short answers about Cleric by topic. Each topic names the page that covers it in full; fetch only the ones your question needs. Index of every page, including blog posts and resources: https://cleric.ai/llms.txt

Every public page has a Markdown version at the same path with `.md` appended (the home page is https://cleric.ai/index.md), and returns that Markdown when requested with `Accept: text/markdown`. Machine-readable discovery: https://cleric.ai/.well-known/api-catalog (RFC 9727).

## What Cleric does

Cleric checks every change you ship in production, catches the regressions your tests missed, takes the pager for your alerts, and brings you the cause, the evidence, and a fix PR to review. After the fix deploys, it checks that the issue is resolved. Engineers work with Cleric in Slack, Microsoft Teams, and the Cleric web app. Coding agents such as Claude Code and Cursor can pull Cleric's investigations over MCP. The Cleric docs have their own public MCP server. Cleric verifies each change for up to 14 days after it deploys, and investigates alerts, monitors, and failed deploys as Issues.

- [Home](https://cleric.ai/index.md): What Cleric does: checks every production change, takes the pager, and brings you fixes to review.
- [Product](https://cleric.ai/product.md): Change verification, alert handling, and the two ways to fix: in Cleric or with your coding agent.
- [Technology](https://cleric.ai/technology.md): How Cleric investigates, verifies outcomes for up to 14 days, and improves from verified results.
- [The verification loop](https://cleric.ai/verification-loop.md): How Cleric refines, deploys, monitors, and verifies agent-written changes in production.
- [How investigations start](https://docs.cleric.ai/usage/how-investigations-work.md): Every way an issue is created, and what Cleric does from investigation to resolution
- [Triggers and alert routing](https://docs.cleric.ai/setup/agents-and-triggers.md): How Slack and Microsoft Teams alerts are matched to agents, and how scheduled runs work

## Pricing and trial

Pricing is public: plans from $700/month, paid in credits for completed work. Every trial includes 1,000 evaluation credits after activation approval, with no time limit.

- [Pricing](https://cleric.ai/pricing.md): Plans from $700/month, what each unit of work costs in credits, and the pricing FAQ.
- [Investigation cost calculator](https://cleric.ai/calculator.md): Estimate the annual cost of production investigations for your team, with the formulas.

## Integrations and MCP

- [Integrations](https://cleric.ai/integrations.md): What Cleric connects to by category, MCP for anything else, and integrations built on request.
- [Supported integrations](https://docs.cleric.ai/integrations/supported-integrations.md): Every supported integration, including Slack, Microsoft Teams, GitHub, and Kubernetes, with setup steps and the permissions each one needs. This list is authoritative
- [Integrations overview](https://docs.cleric.ai/integrations/overview.md): What Cleric reads from each integration
- [Cleric Docs MCP Server](https://docs.cleric.ai/~gitbook/mcp): Public, no sign-in, streamable HTTP. Search and read the Cleric docs from any MCP client. Add this URL as an HTTP MCP server
- [Cleric MCP Server setup](https://docs.cleric.ai/integrations/mcp.md): The product MCP server runs on each customer's instance at https://<your-company>.app.cleric.ai/mcp and authenticates with a personal API key. Covers setup in Claude Code and Cursor, and every tool and prompt

## Security and data

Read-only by default, with write access when you're ready. SOC 2 Type II. US or EU hosting. Customer data is never used for training.

- [Security and data privacy](https://docs.cleric.ai/security/security-data-privacy.md): Access model, data retention, model providers, hosting, SOC 2 Type II, and penetration testing
- [EU hosting and data processing](https://docs.cleric.ai/security/eu-hosting-data-processing.md): What is processed in the EU and what is processed in the US
- [Subprocessors](https://trust.cleric.ai/?tab=subprocessors): Third parties that process customer data, listed in the Trust Center. The page needs JavaScript
- [FAQ](https://docs.cleric.ai/reference/faq.md): Short answers on access, permissions, and data
- [Trust Center](https://trust.cleric.ai): SOC 2 report, policies, and penetration test reports. The page needs JavaScript; read Security and data privacy above for a text summary
- [Vulnerability disclosure policy](https://cleric.ai/vulnerability-disclosure.md): How to report a security vulnerability to security@cleric.ai, response targets, and safe harbor

## Customers and results

Used by engineering teams at Fubo, LaunchGood, ASAPP, Mixmax, and Mr. Delivery. As of October 2026 (Cleric production data across customer tenants, measured Oct 15, 2026): Cleric closed 58% of alerts on its own: it investigated them, found no action was needed, and closed them without an engineer replying. Alerts across customer tenants, May 20 to Oct 15, 2026. "No action needed" is Cleric’s own verdict. Engineers rated Cleric 4.08 out of 5, with 83% of ratings at 4 or 5 stars. Optional ratings from customer engineers, Sep 15 to Oct 14, 2026. In Q3 2026, 51% of resolved incidents were fixed by applying Cleric’s suggested remediation. Resolved incidents across customer tenants, Q3 2026, as judged by Cleric’s resolution monitor. How we verify Cleric’s fixes: https://cleric.ai/blog/verifying-fixes.

- [Case study: How the World’s Leading Community-Based Travel Network is Transforming Incident Response with AI](https://cleric.ai/resources/case-studies/how-the-worlds-leading-community-based-travel-network-is-transforming-incident-response-with-ai.md): BlaBlaCar is partnering with Cleric to bring AI-driven incident response to scale. Learn how their engineering team is using AI to reduce alert fatigue, accelerate root cause analysis, and support fast, reliable operations across a complex global platform.

## Use cases

- [Alert Investigation](https://cleric.ai/solutions/alert-investigation.md): Automate alert investigations for fast, clear answers
- [Cloud Infrastructure](https://cleric.ai/solutions/cloud-infrastructure.md): Diagnose failures in any cloud environment
- [Debug Deployment Failures](https://cleric.ai/solutions/deployment-failures.md): Catch and fix bad deploys before they escalate
- [Fix Recommendation](https://cleric.ai/solutions/fix-recommendation.md): Get precise fix recommendations tailored to your stack
- [For Engineering Leaders](https://cleric.ai/solutions/for-engineering-leaders.md): Resilient systems, happier teams
- [For Engineers](https://cleric.ai/solutions/for-engineers.md): Stay in flow, let Cleric take the hit
- [For SREs](https://cleric.ai/solutions/for-sres.md): Make on-call suck less
- [Incident Triage](https://cleric.ai/solutions/incident-triage.md): Filter noise for critical incident triage
- [Kubernetes](https://cleric.ai/solutions/kubernetes-environments.md): Find and fix Kubernetes failures fast
- [Microservices](https://cleric.ai/solutions/microservices-architectures.md): Trace and resolve issues across services
- [On-Call Automation](https://cleric.ai/solutions/on-call-automation.md): Automate on-call response to slash burnout
- [Root Cause Analysis](https://cleric.ai/solutions/root-cause-analysis.md): Uncover root causes with clear, evidence-backed analysis

## Comparisons

- [Cleric vs AWS DevOps Agent](https://cleric.ai/compare/aws.md): AWS gives you an agent. Cleric gives you a learning system for production.
- [Cleric vs Azure SRE Agent](https://cleric.ai/compare/azure.md): Azure gives you an Azure agent. Cleric gives you a learning system for production.
- [Cleric vs Bits AI SRE](https://cleric.ai/compare/datadog.md): Bits AI SRE investigates inside Datadog. Cleric investigates all of production.
- [Cleric vs Grafana Assistant](https://cleric.ai/compare/grafana.md): Grafana investigates inside Grafana. Cleric gives you a learning system for production.
- [Cleric vs PagerDuty SRE Agent](https://cleric.ai/compare/pagerduty.md): PagerDuty gives you an incident agent. Cleric gives you a learning system for production.

## Glossary

- [What is a Self-Learning AI SRE?](https://cleric.ai/glossary/what-is-a-self-learning-ai-sre.md): A self-learning AI SRE is an AI SRE that improves investigation quality over time by learning from prior investigations, engineer feedback, and environment changes. The operative phrase is over time. Not every agent that uses an LLM does this.
- [What is Alert Fatigue?](https://cleric.ai/glossary/what-is-alert-fatigue.md): Alert fatigue is what happens when engineers are exposed to so many low-value alerts that they stop trusting the alert stream. The problem is not only noise. It is the learned belief that most interruptions are not worth the interruption.
- [What is an AI SRE?](https://cleric.ai/glossary/what-is-an-ai-sre.md): An AI SRE is an AI agent used in production operations to investigate issues, gather evidence across tools, explain likely causes, and surface emerging risks before they become incidents. Alerts are one input, not the whole job.
- [What is Cross-Service Incident Correlation?](https://cleric.ai/glossary/what-is-cross-service-incident-correlation.md): Cross-service incident correlation is the work of connecting alerts across multiple services to determine whether they are separate problems or different symptoms of the same underlying issue. In distributed systems, getting this wrong wastes a lot of engineering time.
- [What is Episodic Memory in AI SRE?](https://cleric.ai/glossary/what-is-episodic-memory-in-ai-sre.md): Episodic memory is Cleric’s term for the stored history of past investigations: what happened, what was checked, what the evidence showed, and how engineers corrected the system. It is how repeated incidents stop looking brand new.
- [What is Investigation Memory?](https://cleric.ai/glossary/what-is-investigation-memory.md): Investigation memory is the retained history of how prior incidents were investigated: the alerts, hypotheses, evidence, findings, and corrections. It is the case history an AI SRE uses so recurring problems do not require full rediscovery.
- [What is Operational Memory?](https://cleric.ai/glossary/what-is-operational-memory.md): Operational memory is Cleric’s term for the operational context an AI SRE accumulates over time: environment structure, past investigations, and debugging procedures. The point is simple. The system should not start from zero on every alert.
- [What is Procedural Memory in AI SRE?](https://cleric.ai/glossary/what-is-procedural-memory-in-ai-sre.md): Procedural memory is Cleric’s term for reusable debugging know-how: the investigation order, checks, and heuristics teams use on recurring problem types. It is not just what the system knows. It is how the system approaches the work.
- [What is Root Cause Analysis?](https://cleric.ai/glossary/what-is-root-cause-analysis.md): Root cause analysis (RCA) is the work of identifying the conditions that actually caused an incident instead of stopping at the symptom that surfaced first. In production, that often means following a causal chain across code, configuration, infrastructure, and timing.
- [What is Semantic Memory in AI SRE?](https://cleric.ai/glossary/what-is-semantic-memory-in-ai-sre.md): Semantic memory is Cleric’s term for the infrastructure and environment context an AI SRE uses during investigations: services, dependencies, ownership, configuration, and recent changes. It is the part that helps the system orient before it starts guessing.
- [What is Tribal Knowledge in SRE?](https://cleric.ai/glossary/what-is-tribal-knowledge-in-sre.md): Tribal knowledge in SRE is the undocumented operational judgment engineers accumulate about a production environment: what is normal, what is dangerous, and where to look first when something breaks. It is useful, fragile, and expensive when it stays trapped in people’s heads.

## Company and contact

Cleric is made by Agentik, Inc., founded in 2023 and based in San Francisco. CEO and co-founder: Shahram Anver. Raised $9.8M from Zetta Venture Partners and Vertex Ventures US. Named a Gartner Cool Vendor in AI for SRE and Observability (2025). Start a free trial: https://cleric.ai/start. Explore the product: https://demo.cleric.ai. Book a demo: https://cleric.ai/demo. Contact: https://cleric.ai/contact. An agent may help fill out the form, but a verified person signs in and remains accountable. Declare the company or person you represent in the optional field. Tenant setup and trial credit activation are separate approvals. Uncertain requests reach human review; contact support@cleric.ai for help.

- [About](https://cleric.ai/about.md): Cleric’s mission, origin, team, funding, and investors.
- [Careers](https://cleric.ai/careers.md): Open roles at Cleric (in person, San Francisco), what the team is building, and benefits.
- [Contact](https://cleric.ai/contact.md): How to reach sales, support, security, press, and careers, plus the mailing address.
- [Book a demo](https://cleric.ai/demo.md): Book a 30-minute demo with Cleric to discuss your production environment.
- [Blog](https://cleric.ai/blog.md): All Cleric blog posts, newest first.
- [Resources](https://cleric.ai/resources.md): Webinars, case studies, and guides on production reliability for AI-built software, change verification, incident investigation, and operational memory.
- [Changelog](https://docs.cleric.ai/reference/changelog.md): What has shipped and when
- [Status](https://status.cleric.ai): Current availability and incident history for Cleric services