Autoptic Unveils Three Breakthrough AI Capabilities to Extend DevOps Change Resilience

New platform features empower engineering teams to better detect, understand, and act on AI-driven production changes before they become customer-impacting incidents.

Austin, Texas, USA. May 5, 2026. At DevOpsDays Austin 2026, Autoptic today announced three new artificial intelligence (AI) capabilities in its DevOps Change Resilience Platform, advancing the company’s mission to help modern software teams better detect and address lurking, latent production problems before they escalate into incidents. At a time when change velocity is rapidly accelerating, and AI-powered development is mainstream, Autoptic’s latest capabilities provide a more open, intelligent, and efficient approach to monitoring, managing, and mitigating the risks associated with production changes.

In 2026, 42% of production code is fueled by AI (source: Sonar). Pull requests per author are up 20% (source: Cortex). At the same time, AI code is generating 1.7x more issues than human-only code (source: CodeRabbit). Change failure rates remain high: 10%+ for the majority of teams (source: Google DORA). Production incidents per pull request are up 23.5% (source: Cortex). And incidents are more expensive than ever: $1.5M/hour for large enterprises (source: BigPanda).

Three new Autoptic AI DevOps capabilities help tackle these challenges: Open Agent Orchestration, the Change Resilience Catalog, and Dynamic Outlier Detection. They work together with the broader Autoptic platform to give engineering teams earlier visibility into risk, more cost-effective pathways to automation, and greater control over how AI operates within their environments.

“Autoptic is deeply committed to the quest of DevOps Change Resilience,” stated Autoptic Co-Founder & CTO, Peco Karayanev. “These three capabilities were driven by an obsessive focus on the success of our early customers and design partners. We’re grateful to the software engineering and DevOps leaders who helped us imagine, build, and test these advancements.” 

Open Agent Orchestration

Open Agent Orchestration (“Orchestration”) builds a flexible harness that enables teams to centralize and manage intelligent AI Agents, Skills, and Tools across their environments and cross-system workflows. With Autoptic Orchestration, teams are not locked into a single model or vendor for AI DevOps or AI SRE initiatives. Instead, they can design, deliver, and manage AI workflows that align with their architecture, security posture (including SSO), operational preferences, and token-cost appetite, inclusive of using open source (OSS) LLMs running within a virtual private cloud (VPC).

Autoptic Orchestration includes:

• Universal MCP agent, allowing enhanced context between Autoptic and external MCP-enabled systems

• Claude Code Managed Agents integrations, enabling advanced AI-assisted workflows

• Ongoing support for both open source and proprietary language models and agents, including OpenAI gpt-oss and OpenClaw, deployable locally or accessed via cloud inference services 

For Standard Skills, Autoptic Orchestration runs step-by-step, harnessed workflows that connect Tools to data Sources. These workflows can follow a fixed plan or adapt as they go. Each step uses results from earlier steps to improve accuracy. Detective Skills add a higher-level loop. A supervising LLM decides whether to finish or call other approved Skills, then combines their results and continues until it hits an intelligent limit (like time or budget). This gives teams two layers of capability: execution within a Skill and coordination across Skills. Everything is tracked with detailed metadata, including what decisions were made.

With Open Agent Orchestration, agentic AI applied to DevOps is:

• Controlled and predictable: workflows follow defined Skills and allowed actions. Behavior stays within harnesses and guardrails. Token costs are managed

• Executed efficiently: tasks run automatically end-to-end, reducing manual work 

• Flexible and malleable: the system can adapt, replan, and explore options 

• Clearly visible: every step, decision, and outcome is logged for auditing and debugging

In short, Autoptic Orchestration gives engineering teams automation they can trust, an open approach to agents and inference models, and the flexibility to balance real-world complexity with much-needed controls. 

Change Resilience Catalog

The Change Resilience Catalog (“Catalog”) is a curated library of ready-to-use Agents, Skills, and Tools for the Autoptic system. Instead of building utilities from scratch, software development, SRE, and DevOps teams pick the foundational elements they want, contextualize them on the fly, attach them to how the team works, and then update them as needed. 

Each Autoptic Catalog item is maintained as a reusable building block. Teams can mix and match Agents and Skills with the Tools and data Sources they rely on. The Autoptic environment then reflects Catalog choices, providing a fast path from “what we need” to “what is running,” without the requirement to repeatedly redefine patterns. 

The Autoptic Catalog provides:

• 15+ Data Sources: direct integrations with leading DevOps tools including the AWS stack, Datadog, GitHub, GitLab, Grafana, OpenSearch, Prometheus, and Jira  

• 40+ Tools: deterministic scripts, built using a precise and efficient domain specific language (DSL) from Autoptic (PQL), editable using AI coding systems like Claude

• 30+ Skills: specific tasks that bridge Tools and Agents, ensuring predictable pathways that automate and align with the core jobs that DevOps engineers, SREs, platform engineers, and other operational leaders perform regularly 

• 10+ Agents: comprehensive AI entities that proactively deliver intelligent Briefs and fuel on-demand Investigations

These Autoptic components span performance monitoring, production risk detection, observability cost optimization, and other use cases. All Change Resilience Catalog items are accessible directly within the Autoptic Platform and through Autoptic Docs.

The result: engineering teams spend less time constructing agents, tuning skills, automating tools, and wiring systems together—spending more time confidently operating change resilient, high-performing software environments.

Dynamic Outlier Detection

Dynamic Outlier Detection (“Detection”) is an innovative approach to identifying meaningful signals of volatility within complex telemetry. It is based on change-correlated outliers. Rather than relying on static thresholds, Autoptic Detection aggregates, compresses, and analyzes telemetry streams using a combination of AI inference and a series of deterministic, signal-conditioning algorithms. 

Detection is built for operational questions in a time window: what shifted, where did it happen, and what is most important across entities and their signals. At fleet scale, evaluations across tens of thousands of monitored items (metrics, patterns, related series, etc.) surface statistical outliers and critical patterns by ranking what matters instead of expecting humans to guess at thresholds.

Autoptic Dynamic Outlier Detection combines relative scale with evidence of recent changes, separates localized spikes from fleet-aligned movements, and pairs notable shifts with baseline context, delivering concise, scannable Briefs suitable for action and follow-up.

Holistically, this enables the Autoptic system to establish relative, context-aware volatility thresholds and to deliver intelligent Briefs to engineering operations teams. In practice, this means:

• No more guesswork in setting alert thresholds

• Fewer false positives and missed signals

• Earlier detection of subtle production risks

• Additional confidence via a new layer of watchfulness

With Dynamic Outlier Detection, Autoptic listens to the hard-to-spot signals of telemetry patterns, surfacing volatile issues and degradations before they become incidents and outages, complimenting and extending existing observability and monitoring systems.

A New Standard for DevOps Change Resilience

Together, these capabilities progress how teams manage production change. Autoptic’s platform is designed to meet engineering organizations where they are today: integrating with existing DevOps tooling, supporting Bring Your Own Cloud (BYOC) deployments, and aligning with modern security and data governance requirements.

For software engineering teams, Autoptic delivers:

• AI DevOps automation to match the demands and opportunities of AI-powered software development

• Early detection of lurking production degradations before they escalate

• Operational answer access, democratized across engineering and platform teams

• Cost visibility and control, with fixed, predictable pricing that avoids the token and usage sprawl of other industry products

About Autoptic

Autoptic is collaboratively designing and building the concept of DevOps Change Resilience, helping CTOs and their software engineering teams move faster, absorb more change, calibrate risk, and become stronger in a world of increasingly complex, AI-fueled systems. It is co-founded by 20+ year software industry veterans and backed by 30+ investors and advisors. The company was recently named one of 12 “Austin Startups to Watch in 2026” by the Business Journal. To learn more about Autoptic, visit www.autoptic.ai and request a personalized demo led by a senior software engineer.

Image above courtesy of Logan Voss via Unsplash.