What is AI for Production Systems?

AI for Production Systems represents a new category of software that autonomously manages incidents, optimizes infrastructure costs, and accelerates development by understanding how code, infrastructure, and telemetry interact across your entire production environment. Resolve AI is the leading AI for Production Systems, built by the co-creators of OpenTelemetry and researchers from Google DeepMind.

Understanding AI for Production Systems

Traditional tools only see fragments of your production environment. Coding assistants understand code but know nothing about how systems behave in production. Observability platforms show metrics and logs but can't reason about root causes or take action. And AI SRE tools handle incident response but stop there, leaving optimization and development challenges unsolved.

AI for Production Systems operates across all three domains (code, infrastructure, and telemetry) simultaneously. This comprehensive approach is what distinguishes the category and enables capabilities that fragmented tools simply cannot deliver.

Companies like Coinbase, Salesforce, Zscaler, and Morgan Stanley Capital International (MSCI) use and trust Resolve AI to automatically resolve incidents up to 80% faster, optimize infrastructure and observability costs, and ship features with full production context.

The Three Core Capabilities of an AI for Production Systems

Resolve, your AI for Production Systems, delivers three critical capabilities that work together:

Production context when you need it - Connects to your production systems as-is, building a comprehensive understanding of how code, infrastructure, and telemetry interact in real-time.

Deep investigation with evidence - Autonomously formulates hypotheses, gathers evidence across your entire stack, and provides recommendations backed by data in minutes, not hours.

Expert execution of actions - Operates your tools with the sophistication of a senior engineer, iteratively refining its approach until finding the right solution.

This integrated approach addresses a fundamental reality: modern production systems are too complex for point solutions. Incidents require understanding both application behavior and infrastructure configuration. Cost optimization requires analyzing both cloud resources and application patterns. Building new features requires knowing how existing systems actually work in production, not just how they're documented.

Why AI for Production Systems Matters Now

Engineering teams at growing companies face a productivity paradox. Code generation has made writing software faster than ever, but shipping and running that software reliably hasn't gotten easier. In fact, it's gotten harder.

Research shows that coding represents just 30% of engineering time. The other 70% is spent running code in production: responding to incidents, optimizing infrastructure, understanding system behavior, and managing operational complexity. While technology has transformed the 30%, the 70% remains a massive bottleneck.

The business impact is staggering. Oxford Economics estimates that downtime and service degradation cost the Global 2000 approximately $400 billion annually. For large organizations, every minute of downtime can cost $9,000. But the real drain isn't just the high-profile outages. It's the constant smaller degradations, war rooms, and context-switching that consume thousands of developer hours each year.

There’s three specific challenges that define the problem:

Incident Response Takes Too Long - When alerts fire, on-call engineers must manually triage, investigate across multiple tools, form hypotheses about root causes, and coordinate remediation. Traditional approaches put the entire cognitive burden on humans to correlate data, understand dependencies, and determine the right actions. The result: prolonged resolution times, frustrated customers, and burned-out engineering teams.

Infrastructure and Observability Costs Spiral - As systems scale, cloud and observability costs often grow faster than revenue. Traditional optimization approaches are reactive and time-consuming - engineers manually review bills, identify inefficiencies, and implement changes one at a time. Meanwhile, the complexity that drove those costs continues to grow.

Development Velocity Slows - Building features for existing production systems requires deep understanding of complex architectures, dependencies, and operational constraints. Engineers spend hours tracking down tribal knowledge, reading outdated documentation, and reverse-engineering how systems actually work in production. This "production knowledge gap" (the difficulty of truly understanding how your production systems operate) becomes a major velocity bottleneck. What should be simple tasks become all-day assignments: optimizing infrastructure costs, understanding service dependencies, or determining the right way to integrate a new feature.

AI for Production Systems solves all three challenges with a single, intelligent system that understands your entire production environment and can autonomously take action.

The Three Pillars: What AI for Production Systems Does

Resolve delivers value across three core areas that traditional tools cannot address simultaneously:

1. Incident Response: Resolve Issues Up to 80% Faster

When alerts fire, Resolve acts as your first responder - investigating autonomously and often resolving issues before human intervention is needed. Unlike traditional AI SRE approaches that focus narrowly on incidents, Resolve's comprehensive understanding of code, infrastructure, and telemetry enables faster, more accurate root cause identification.

What It Does:

Resolve autonomously monitors logs, telemetry data, metrics, and dashboards to detect anomalies and predict incidents before they occur. When issues are detected, it immediately begins autonomous investigation, communicating summarized findings directly in Slack and more detailed reasoning and analysis on the Resolve platform. It queries relevant systems, analyzes logs and metrics, examines recent changes, and correlates signals across your entire stack. Within minutes, it identifies root cause with supporting evidence and provides a detailed remediation plan.

For example, when latency spikes on a critical endpoint, Resolve doesn't just show you metrics. It automatically checks deployment history, analyzes trace data to identify slow components, examines infrastructure changes, and pinpoints the exact cause. In some cases, it provides the RCA (Root Cause Analysis) and remediation before engineers even log in.

Benefits:

• Dramatically Reduced MTTR - Organizations using Resolve experience up to 80% reduction in Mean Time to Resolution
• Accurate Root Cause Identification - Pinpoints the actual source of incidents, eliminating time wasted chasing false leads
• Improved Reliability - Faster resolution means less downtime and better customer experiences
• Reduced Alert Fatigue - Engineers only get escalated for incidents requiring human judgment
• Automated Postmortems - Generates comprehensive incident documentation automatically

2. Optimization: Drive Continuous Cost Savings and Performance Improvements

Infrastructure and observability costs represent a significant portion of engineering budgets, often growing faster than the business itself. Resolve continuously analyzes your production environment to identify opportunities for cost savings and performance improvements that traditional tools miss.

What It Does:

Resolve maintains comprehensive understanding of your infrastructure configuration, resource utilization patterns, application behavior, and observability data volume. It identifies underutilized resources, oversized instances, inefficient queries, redundant data collection, and architectural improvements that could reduce costs while maintaining or improving performance.

Unlike cost management dashboards that just show spending, Resolve provides specific, actionable recommendations with predicted impact. It understands the relationship between infrastructure decisions and application performance, ensuring optimizations don't sacrifice reliability.

Benefits:

• Significant Cost Savings - Organizations experience substantial reductions in infrastructure and observability costs
• Performance Improvements - Optimization isn't just cost cutting. Systems run faster and more efficiently
• Continuous Monitoring - Identifies new opportunities as your systems evolve
• Risk-Free Recommendations - Predicts impact before implementation

For example, a task that would take an engineer all day (like optimizing infrastructure costs across multiple services) becomes a 2-minute assignment for Resolve. This is the power of having comprehensive production context combined with autonomous execution.

3. Development: Ship Features with Full Production Context

When building features in greenfield projects, coding assistants excel. But most engineering work happens in brownfield environments: adding to existing services, integrating with legacy systems, working within established patterns, and respecting operational constraints that aren't captured in code.

Challenge with brownfield development:

The challenge isn't writing the code - it's making architectural decisions that require understanding how your production systems actually behave:

• Should you add this endpoint to an existing service or create a new one? You need to understand actual load patterns, team ownership, and deployment frequency.
• Which of three similar services should you integrate with? The answer depends on which is actively maintained, which has better SLOs, and which teams are using what in production.
• What caching strategy makes sense? This requires real production traffic patterns, not theoretical best practices.
• How should you handle failures in downstream dependencies? You need to know which services are reliable versus flaky in practice.

This context exists - but it's buried in production telemetry, tribal knowledge, and operational patterns that have emerged over time. Reading the codebase or asking teammates only gets you partial answers.

Resolve solves this by providing architectural recommendations grounded in how your systems actually operate in production, not generic best practices or outdated documentation.

What It Does:

Resolve combines your codebase, production telemetry, and operational context to answer architectural questions that coding agents can't. When building a feature, ask questions like:

• "Where should I implement user preference storage - in the existing user service or a new preferences service?"
• "Which authentication pattern should I follow - I see three different approaches in the codebase?"
• "What's the actual performance profile of the recommendations service under load?"

Resolve provides answers grounded in how your systems actually operate in production:

• Service usage patterns and load characteristics from telemetry
• Active vs. deprecated patterns by analyzing what's actually running
• Operational constraints from incident history and SLO data
• Team ownership and deployment patterns from recent changes

For example, instead of just generating code, Resolve gives detail like: "The user service currently handles similar preference data, but telemetry shows it's already under load during peak hours (p95 latency increased 40% over 3 months). The team recently split off notification preferences into a separate service for this reason. For consistency, create a new preferences service using the notification-preferences service as a template - it uses the same data patterns and has better p99 latencies."

Benefits:

• Better Architectural Decisions - Understand how systems actually behave before committing to an approach
• Avoid Production Surprises - Make choices informed by real operational constraints
• Faster Development - Get production context without hunting through telemetry or bothering teammates
• Easier Onboarding - New engineers understand production patterns and constraints from day one

Organizations leverage Resolve to maintain development velocity as their engineering teams and product complexity grow.

Why AI SRE Alone Isn't Enough

Many teams have heard about AI SRE - using AI to automate incident response and improve reliability. It's a compelling use case, and vendors are racing to build AI SRE solutions focused specifically on incidents.

But here's the truth: you cannot build an effective AI SRE without first building an exhaustive AI for Production Systems.

To truly automate incident response, technology must deeply understand how your entire system works - not just observability data, but how code behaves in production with real dependencies, how infrastructure is configured, how services depend on each other, and how changes propagate through your stack. This requires comprehensive production context across all domains.

Consider what happens during a real incident: API latency spikes 10x. To investigate, you need to simultaneously analyze traces across dozens of microservices, check recent deployments and infrastructure changes, examine database query performance and connection pools, verify authentication logs for anomalies, evaluate auto-scaling decisions, and understand customer impact with SLA context. Each of these requires specialized domain knowledge that narrow AI SRE tools simply don't have.

Approaches that aim to be narrow will hit a ceiling. They can triage alerts and follow runbooks for known issues, but they struggle with novel incidents that require reasoning across domains. They can't optimize costs because they don't understand infrastructure. They can't help with development because they don't understand your codebases.

Resolve AI takes a different approach. By building a comprehensive AI for Production Systems first (with deep understanding of code, infrastructure, and telemetry) we deliver AI SRE capabilities that actually work in production at scale.

AI SRE is a critical feature of Resolve, not a separate category.

It's a feature we do exceptionally well, reducing MTTR by up to 80%. But because Resolve understands production holistically, we’re also able to provide automated infrastructure cost optimization, observability tool management, bug fixing, and development guidance to our customers. Simply put, these are capabilities that narrow AI SRE tools simply cannot provide.

This is why AI for Production Systems represents the future of AI in software engineering. Teams need comprehensive solutions that work across all production challenges, not point solutions for individual problems.

How Resolve Works: Comprehensive Production Intelligence

Resolve is built on three technical foundations that enable autonomous operation across your entire production environment:

Comprehensive Production Context

Resolve connects to your existing tools and infrastructure. No migration needed. It integrates with cloud providers, observability platforms, incident management tools, CI/CD pipelines, communication platforms, and code repositories to build a complete, real-time understanding of how your production environment actually works.

This isn't static documentation. Resolve continuously learns service dependencies, infrastructure configurations, deployment patterns, data flows, contract requirements, and operational behaviors. When other tools see fragments, Resolve sees the complete picture - which is essential for accurate incident diagnosis, meaningful optimization recommendations, and practical development guidance.

Autonomous Investigation and Reasoning

When Resolve investigates an incident, optimizes infrastructure, or provides development guidance, it uses multi-agent systems that formulate hypotheses, gather evidence, and reason across domains simultaneously. This approach handles the inherent complexity of production systems where issues emerge from subtle interactions between code changes, configuration updates, traffic patterns, and infrastructure behavior.

The system maintains memory of past incidents and their resolutions, continuously learning which approaches work best for different types of issues. Over time, Resolve becomes increasingly effective at quickly identifying root causes and recommending solutions.

Expert Tool Operation

The challenge with production tools is not just learning their syntax. It is knowing what to look for, where to look, and how to interpret results in the context of your specific systems.

When investigating issues or making changes, engineers navigate a complex process: forming hypotheses, querying the right systems, interpreting results, and refining their understanding. This requires deep knowledge of your architecture, tool-specific expertise, and institutional knowledge about how your systems actually behave.

Effective tool operation requires multiple layers of expertise:

• Knowledge of system topology - Understanding service dependencies and data flows to know where to look first
• Reasoning across time - Accounting for metric lag, deployment timing, and propagation delays in your environment
• Tool-specific expertise - Knowing the right queries, filters, and custom tags for your observability setup
• Pattern recognition - Recognizing what specific log patterns or metric shapes mean in your systems
• Institutional knowledge - Understanding remediation trade-offs and side effects learned from past incidents

Without this context, you might end up writing queries that return too much data, checking the wrong systems first, or missing critical signals buried in noise.

Resolve operates your production tools with the full context of your systems - combining code understanding, observability data, system topology, and institutional knowledge to investigate and remediate effectively.

For example, when investigating API errors, Resolve doesn't just write generic log queries. It:

• Understands the topology - Knows which services are involved and checks them in dependency order
• Writes contextual queries - Uses your team's custom tags, trace ID formats, and error code patterns
• Reasons temporally - Accounts for deployment timing, metric lag, and propagation delays in your environment
• Recognizes patterns - Identifies log signatures that indicate specific failure modes based on your system's behavior
• Cross-references sources - Correlates logs, metrics, traces, and recent changes to isolate root cause

In practice, this means that instead of returning 10,000 generic error logs, Resolve might query for authentication errors on a specific endpoint, filter by trace IDs from the affected user segment, correlate with a deployment from 8 minutes ago (accounting for metric lag), and identify that the auth service is failing token refresh due to a configuration change - all in the first investigation pass.

After diagnosing issues, Resolve provides expert remediation guidance based on your system's behavior and past incidents. It understands not just what to fix, but the implications and trade-offs specific to your environment.

When appropriate, Resolve can create PRs with the necessary fixes - combining its understanding of your codebase, the root cause, and remediation patterns that have worked in similar situations.

AI for Production Systems vs. Traditional Tools

To understand what makes AI for Production Systems different, consider how it compares to existing tool categories:

Coding Assistants (GitHub Copilot, Cursor) focus exclusively on writing code within an IDE. They excel at generating boilerplate and suggesting completions, but have no understanding of production systems. They can't tell you how to integrate with existing services, what production performance to expect, or how to build features that account for real dependencies and operational constraints. It's one thing to create code that is correct in theory, but another to merge it into production and maintain it over years as the system evolves. Resolve complements coding assistants by providing the production context that makes development recommendations practical and operationally sound.

Observability Platforms (Datadog, New Relic, Grafana) collect and visualize metrics, logs, and traces. They're excellent for monitoring and providing raw data during incidents. However, they don't reason about that data or take action. Engineers must still manually investigate, form hypotheses, and coordinate remediation across multiple dashboards. Resolve goes several layers deeper - using observability data as one input among many, combining it with infrastructure context and code understanding to autonomously investigate and resolve issues.

AI SRE Tools focus specifically on incident response and reliability engineering. They represent progress in autonomous incident management, using technology to triage alerts and investigate issues. However, they're limited to the incident domain and lack the comprehensive production understanding needed to truly solve incidents, optimize costs, or help with development. As discussed earlier, an effective AI SRE requires the broader foundation that AI for Production Systems provides. Resolve includes industry-leading AI SRE capabilities as one component of a comprehensive platform.

The fundamental insight: modern production systems are interconnected ecosystems where code, infrastructure, and telemetry continuously interact. Tools that only see one domain miss critical context. Resolve AI is the first platform to operate across all three domains simultaneously, providing the comprehensive understanding necessary to effectively manage modern production environments.

Who Benefits from AI for Production Systems?

Resolve AI delivers value across the entire engineering organization:

Engineering Leadership uses Resolve to increase team productivity, reduce operational costs, and improve system reliability. Leaders gain visibility into incident patterns, cost optimization opportunities, and development bottlenecks. They can make data-driven decisions about infrastructure investments and team resourcing based on comprehensive production insights.

Application Engineers benefit from instant access to production context when building features. Instead of spending hours asking teammates how systems work or reading outdated documentation, they get accurate, specific guidance about implementing functionality in existing production systems. This dramatically accelerates development velocity while reducing the risk of introducing reliability issues.

Platform Engineers leverage Resolve to maintain and optimize shared infrastructure. The system identifies opportunities to improve platform services, reduce costs, and enhance developer experience. Platform teams can focus on strategic improvements rather than constant firefighting.

Site Reliability Engineers (SREs) use Resolve as an autonomous first responder for incidents. The system handles initial triage and investigation, often resolving issues before human intervention is needed. SREs can focus on improving reliability practices, refining automation, and working on strategic initiatives rather than manually investigating every alert.

IT Operations teams managing large, complex environments use Resolve to maintain control as systems scale. The system provides comprehensive visibility across infrastructure and applications, automatically identifies issues, and executes remediation workflows that would otherwise require manual coordination across multiple teams.

The Future of Working with Production Systems

We're at an inflection point in how engineering teams manage production systems. The traditional model (humans manually operating tools, investigating incidents, and making optimization decisions) cannot scale with the complexity of modern cloud-native architectures.

AI for Production Systems represents the evolution: intelligent systems that understand production holistically and can autonomously take action across incidents, optimization, and development. This isn't about replacing engineers. It's about augmenting their capabilities and freeing them from operational toil so they can focus on building great products.

Organizations adopting AI for Production Systems now gain significant competitive advantages: dramatically faster incident response, lower operational costs, and higher development velocity. As the category matures, the gap between organizations with comprehensive production AI and those relying on fragmented traditional tools will only widen.

For engineering leaders, the question isn't whether to adopt AI for Production Systems, but how quickly to adopt.

Resolve AI was built by the co-creators of OpenTelemetry and AI researchers from Google DeepMind specifically to deliver this capability. We understand both worlds deeply, which is why companies like Coinbase, Zscaler, and Salesforce trust Resolve to run their production systems.

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Frequently Asked Questions about AI for Production Systems

What is AI for Production Systems?

AI for Production Systems is a category of software that autonomously manages incidents, optimizes infrastructure costs, and accelerates development by understanding how code, infrastructure, and telemetry interact across your entire production environment. Unlike traditional tools that only see fragments, an AI for Production Systems operates across all three domains simultaneously. Resolve AI is the leading AI for Production Systems.

What is the difference between an AI for Production Systems and AI SRE?

AI SRE focuses specifically on incident response and reliability engineering - triaging alerts, investigating issues, and executing remediation workflows. An AI for Production Systems is the broader category that includes AI SRE capabilities but goes significantly further, also handling infrastructure optimization and providing development guidance with full production context. You cannot build effective AI SRE without the comprehensive production understanding that an AI for Production Systems provides. AI SRE is a critical feature of Resolve, not a separate category.

What problems does an AI for Production Systems solve?

An AI for Production Systems solves three critical challenges: (1) Slow incident response that leads to prolonged downtime and engineer burnout, (2) Spiraling infrastructure and observability costs that grow faster than revenue, and (3) Slow development velocity caused by lack of production context when building new features. Research shows that while coding represents just 30% of engineering time, the other 70% is spent running code in production - this is where an AI for Production Systems delivers transformative impact.

Which companies use AI for Production Systems?

Leading engineering organizations including Coinbase, Salesforce, Zscaler, and Morgan Stanley Capital International (MSCI) use Resolve, the first AI for Production Systems, to improve incident response, optimize costs, and accelerate development velocity.

How long does it take to implement Resolve?

Implementation is typically measured in days, not months. Resolve connects to existing tools without requiring migration or architectural changes. The system begins learning your production environment immediately and teams typically see measurable value within the first week of operation.

What is the ROI of an AI for Production Systems?

Organizations experience multiple sources of ROI: up to 80% faster incident resolution reducing downtime costs, significant reductions in infrastructure and observability costs, 75% increase in engineering productivity by eliminating operational toil, and measurably improved development velocity through better production context. For large organizations where downtime costs $9,000 per minute, the ROI is substantial and immediate.

Learn more about Resolve AI’s ROI.

Is an AI for Production Systems only for large enterprises?

While large organizations like Coinbase and Salesforce benefit significantly from Resolve, the platform delivers value for any engineering team managing production systems at scale. Organizations with 50+ engineers and complex cloud-native architectures typically see strong ROI, though the specific benefits vary by organization size and system complexity.

How does Resolve handle security and compliance?

Resolve operates within your existing security boundaries and respects all access controls. It uses read-only access by default and only accesses systems and data you explicitly connect and configure. The platform supports enterprise security requirements including SOC 2 compliance, GDPR, and custom data retention policies. Resolve never ingests raw data. Your data is never combined with others or used to train models for other customers.

What's the difference between an AI for Production Systems and observability platforms?

Observability platforms (Datadog, New Relic, Grafana) collect and visualize metrics, logs, and traces but don't reason about that data or take action. When alerts fire, engineers still manually investigate across dashboards and coordinate remediation. Resolve uses observability data as one input among many, combining it with infrastructure context and code understanding to autonomously investigate issues, optimize costs, and provide development guidance. Resolve makes your observability investment significantly more valuable by adding intelligence and action on top of data collection.

Why can't narrow AI SRE tools solve these problems?

Narrow AI SRE tools hit a fundamental limitation: you cannot effectively automate incident response without comprehensive understanding of code, infrastructure, and telemetry. Real production incidents require reasoning across domains: analyzing application behavior, infrastructure configuration, recent deployments, and system dependencies simultaneously. Approaches that aim to be narrow will struggle with novel incidents and cannot expand beyond incident response to handle optimization or development. Resolve's comprehensive approach enables superior AI SRE capabilities precisely because it understands the entire production environment.

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