Breaking the Iron Triangle: How AI-powered investigations change the economics of uptime

TL;DR: 

• Observability isn’t about collecting more data, it’s about acting faster and smarter. In today's world, insight is the commodity that matters. 
• If your stack can’t deliver answers fast, it’s just cost without return. Grafana Assistant Investigations, through specialized AI agents, redefines the economics of uptime by shifting this burden from expensive human expertise to cheap compute time. 
• This dramatically reduces Mean Time To Resolution (MTTR) from hours to minutes, empowers junior engineers to resolve issues, and reclaims senior SRE time for strategic, high-value work like system hardening and improving reliability, effectively changing the trade-offs of the Iron Triangle in your favor.

In engineering, there's a concept known as the Iron Triangle. With three sides—cost, quality, time—it's a framework intended to help you prioritize different aspects of project management

Want fast, high-quality features? It'll cost you. Need to keep costs down while maintaining quality? That'll take time. And if you're trying to move fast and cheap? Well, good luck with quality.

For years, this has been the brutal reality of running services on the web. And underpinning all of this is the human cost of observability—the time spent responding to incidents, the expertise bottleneck, and the countless hours burned chasing ghosts in your stack.

According to Grafana Labs' 2025 Observability Survey, engineering teams are facing rising complexity, poor signal-to-noise ratios, and increasing observability costs as systems scale. The data is there. The tools are there. But somehow, incidents still take hours to resolve, alerts still create more noise than signal, and your senior engineers are still spending half their week being human query engines.

Here's the uncomfortable truth: You may not be getting your money's worth from observability.

The triangle hasn't disappeared—it just shifted. You can't escape the trade-offs, but you can change the weight. And right now, time is bleeding you dry.

But it doesn't have to be this way. As we at Grafana Labs continue to build a set of actually useful AI capabilities for observability, we're building a future where the Iron Triangle shifts in your favor.

Why traditional observability is failing

Observability has been sold as a promise: instrument everything, collect all the data, and you'll have visibility into your systems. And technically, we, as an industry, have delivered. You do have visibility.

The problem? Visibility isn't insight.

Let's talk about what actually happens during an incident: Someone gets paged, opens Grafana, clicks through dashboards, writes PromQL queries, correlates metrics with logs, checks traces, realizes it's not what they thought, and starts over. If they're lucky, this takes an hour. More often it's closer to two to four hours.

And here's the kicker: This only works if the person on-call knows which dashboards exist, the query language syntax, the architecture expertise needed to form solid hypotheses, and the tribal knowledge of what "normal" looks like.

What happens when that person is new? Or it's 3 a.m.? Or your best SRE just gave two weeks' notice?

As Neil Wilson, Director of Software Engineering at LexisNexis Risk Solutions, recently told us: "One of the biggest use cases for us is reducing the cognitive load on engineers."

Put another way, the cost isn't the observability platform—it's the human expertise required to use it.

Wilson went on to share how LexisNexis Risk Solutions is using Grafana Assistant, our context-aware AI agent in Grafana Cloud, to help overcome this challenge. "Grafana Assistant helps us get to the root cause faster—without needing deep expertise in every part of our complex system. That lowers training time and reduces risk if one of our experts leaves," he said.

The manual investigation process is bleeding you dry in time and human expertise, which is your most expensive and least scalable resource. This is where AI-driven investigations fundamentally shift the economics. Grafana Assistant Investigations, a new feature within Assistant, moves the pain back to where it belongs: compute time is cheap, human expertise is expensive.

From patterns to profits: when data drives real impact

What if, instead of an engineer spending two hours clicking through dashboards, your observability stack could investigate multiple hypotheses in parallel—across metrics, logs, traces, and profiles—and present findings in minutes?

That's what Assistant Investigations does, now in public preview.

How it actually works

When you hit a complex incident, expand Assistant, click on Investigations and this will open a page where you can describe to Grafana Assistant the issue you are facing—and here's what happens behind the scenes:

Multiple specialized AI agents deploy in parallel:

• A Prometheus agent analyzes your metrics for anomalies
• A Loki agent dives into your logs looking for error patterns
• A Tempo agent traces request paths across services
• A Pyroscope agent examines performance profiles

They don't work in sequence; they work simultaneously. While you're coordinating the incident response, these agents are running targeted queries, identifying correlations, eliminating false leads, and building a comprehensive picture of your system state.

A real example

Let's look at how this works in practice.

Imagine you receive the alert below that signals high latency in your payment service. The following table illustrates the stark difference between the old, manual way of doing things and the rapid, parallel investigation that Assistant Investigations provides.

But here's what's even more important: A junior engineer could do this. They don't need to know PromQL syntax or remember which dashboards to check. The investigation agents do the cognitive heavy lifting.

What you get

When an investigation completes, you get a structured report with:

• Summary: High-level findings and next steps for stakeholders
• Full report: Detailed findings from each agent with exact queries and evidence
• Timeline: Audit trail for post-mortems
• Activity log: Raw events to reproduce any step
• Actionable items: Convert to dashboards, alerts, or work items

This isn't just faster investigation. It's a better investigation—more thorough, more systematic, better documented. Over time, these AI-powered investigations build a valuable knowledge base: connecting patterns across past incidents, highlighting recurring issues, and uncovering opportunities for proactive optimization before the next alert ever fires.

The new economics: from cost center to force multiplier

Let’s look at a different edge of the Iron Triangle. Cost, and how AI powered observability can affect your bottom line:

The traditional math

Observability platform: $50,000-$500,000+/year

Hidden costs (the ones that actually hurt):

• Senior SRE time: $150-$200/hour, fully loaded
• Average incident: 2-4 hours = $300-$800
• 20 incidents/month: $6,000-$16,000/month
• Annual hidden cost: $72,000-$192,000—just in incident response time

That doesn’t include revenue loss during downtime, customer trust erosion, or the opportunity cost of your most experienced engineers spending their time in triage instead of improving reliability or building the next feature.

The new math

With AI-powered investigations:

• MTTR reduction: 2-hour investigations → 20 minutes = 83% time savings
• Democratization: Junior engineers resolve 40% of previously escalated incidents
• Expert time reclaimed: Senior SREs spend 60% less time on triage = ~12 hours/week for strategic work
• Conservative estimate: 50 hours/month of senior engineering time saved
• Value: 50 × $150 = $7,500/month, or $90,000/year in reclaimed expertise

But this isn’t just about saving time. It’s about how that time gets used.

Every hour saved is an hour redirected toward what actually moves the business forward—hardening systems, automating responses, mentoring teammates, and designing for resilience.

Over time, AI-powered investigations create a compounding effect: knowledge accumulates, recurring issues surface faster, and reliability engineering shifts from reactive to proactive.

What this means for your team

The fundamental promise of AI in observability is not a technical one; it’s a shift in organizational leverage. When you solve the Iron Triangle’s time-cost dilemma, you unlock your most valuable resource: your people. This is how that shift manifests across your organization.

For junior engineers

You’re no longer nervous about being on-call. Launch an investigation, follow guided analysis, and resolve issues that used to require escalation.

• Before: “I need to page someone.”
• After: “Investigation found the issue—let me fix it.”

For mid-level engineers

You step into a new role—learning from each investigation, identifying recurring patterns, and proposing automation before the next alert fires.

• Before: “I think I see what’s happening.”
• After: “Here’s the trend—we can prevent it.”

For senior engineers

Stop being the human query engine. Review findings, make strategic calls, and use your expertise to design guardrails and coach others.

• Before: “Let me dig into the data.”
• After: “Here’s how we make sure this never happens again.”

For engineering leaders

The focus of your team shifts–from reactive to proactive.

• Before: “We need another senior hire.”
• After: “Our team is resolving faster, learning faster, and spending more time on the work that drives reliability.”

Addressing the elephant: Is AI actually trustworthy?

Fair question. You've heard AI hype before. Here's why this is different:

1. Grounded in your actual data: Agents run real queries against your real data sources. Every finding includes the exact query and evidence.

2. Human-in-the-loop: You launch, guide, and review. AI augments expertise; it doesn't replace it.

3. Explainable and auditable: Complete timelines of what each agent investigated. Nothing is a black box.

4. Enterprise-ready: Respects existing RBAC, runs within your security model, with granular permissions.

Think of it like a junior engineer who comes to you with findings and says, "Here's what I found in the metrics, logs, and traces. Here are the queries I ran. What do you think?" Except it does it in parallel, across all data sources, in minutes.

The path forward

Seeing is believing.

We've shown how AI‑powered investigations can transform uptime economics, but the best way to internalize this shift is through real‑world experience. To help you prove the ROI and build team confidence, Assistant Investigations is free during Public Preview. Here is a smart, three‑phase plan for rolling it out:

Week 1: Setup and test drive

• Enable Grafana Assistant for your organization (if you have Grafana Cloud, you likely already have access).
• Assign the necessary RBAC roles to a pilot team.
• Select one critical service for the test.
• Launch the first few investigations during live incidents to establish a baseline.

Week 2: Expansion and training

• Train more team members on the investigation workflow.
• Integrate Assistant Investigations into your main on‑call rotation.
• Begin using the structured reports for initial post‑mortem documentation.

Week 3: Measure and commit

• Measure and document key changes, such as mean time to resolution (MTTR) and escalation rates.
• Review the documented evidence from the investigations.
• Use the real ROI data and team confidence to make a strategic decision on continued use before the free period ends.

By the end of the three weeks, you’ll have real ROI data and team confidence—ready to decide whether to keep the tool in production. (We think you’ll love it)

The bottom line

You can't escape the iron triangle. Cost, quality, time—something has to give.

But you can change where the pain lives.

For the last decade, the pain has lived in human time—experts spending hours on incidents, junior engineers unable to contribute, and senior time being diverted from strategic engineering to reactive toil.

Assistant Investigations shifts the pain back to where it belongs: compute time is cheap, human expertise is expensive.

Let AI agents spend computational cycles investigating in parallel. Let your engineers spend their expertise making strategic decisions. Let your junior team members learn by doing. Let your senior engineers work on what matters instead of writing queries at 2 a.m..

The data you're already collecting can work harder for you. The platform you're already paying for can deliver more value. The team you already have can scale further.

The triangle hasn't disappeared. But for the first time in a long time, the trade-offs are changing in your favor.

Start with Grafana Assistant today

Ready to see how your data can work harder? Grafana Assistant and Assistant Investigations are available now in Grafana Cloud. Start with one team, one incident, and see for yourself.

To learn more, check out our technical docs on Assistant, and Assistant Investigations, as well as our announcement blog from ObservabilityCON 2025.

And check out our pricing page for up to date pricing.

Grafana Cloud is the easiest way to get started with metrics, logs, traces, dashboards, and more. We have a generous forever-free tier and plans for every use case. Sign up for free now!