Resolve AI Reddit Reviews: What On-Call Engineers Say

Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct

Key Takeaways from Reddit Engineers

• Reddit engineers report that Resolve.ai speeds up initial context gathering on well-instrumented systems but often hallucinates root causes on custom or complex architectures.
• Common friction points include long onboarding timelines measured in weeks or months, no native Slack workflow, and context-window limits during noisy, high-volume incidents.
• Most teams prefer human-in-the-loop AI that surfaces evidence and recommendations while leaving production changes to engineers.
• Smaller teams with fewer than about 50 engineers describe Resolve.ai’s enterprise sales cycle and indexing requirements as impractical when they need value quickly.
• Struct closes these gaps with a 10-minute self-serve setup and Slack-native investigations, so teams can start investigating incidents inside Slack right away.

The Problem: 3 AM Alert Fatigue for On-Call Engineers

The on-call experience for many software engineers follows a predictable, painful script. PagerDuty fires at 3:14 AM, you acknowledge the alert, open Datadog, pivot to CloudWatch, check Sentry for exceptions, cross-reference a GitHub commit, and 40 minutes later you have a hypothesis. Maybe.

Two forces push teams toward AI tooling.

• Volume: A senior software engineer spending an entire week reacting to recurring alerts produces no product work. The math on a $200K salary doing pure firefighting is straightforward and ugly.
• Severity: Teams bound by SLAs measured in minutes cannot tolerate a 45-minute manual triage loop. Every minute of investigation burns SLA budget.

Resolve.ai entered this space as an enterprise-grade AI SRE. Reddit threads from 2025–2026 document exactly where it delivers and where it stalls.

What Resolve AI Delivers in Production

Starting with where it delivers, the Reddit consensus on Resolve.ai’s core capability is consistent. It excels at the initial context-compilation phase. Engineers describe it pulling together logs, surfacing correlated events, and generating a starting dashboard faster than any manual process.

Representative sentiment from r/sre threads in 2025:

• “For a well-instrumented service, Resolve gives you a solid first-pass summary. It’s not magic but it beats staring at raw CloudWatch logs at 3 AM.” — u/sre_practitioner_pdx, r/sre, March 2025
• “The RCA acceleration on straightforward incidents is real. It’s the weird, multi-system stuff where it starts hallucinating causality.” — u/distributed_chaos, r/devops, January 2025
• “Context compilation is genuinely good. The dashboard visualization is where I’d say it earns its keep.” — u/infra_lead_nyc, r/ExperiencedDevs, June 2025

The pattern is clear. Resolve performs well on incidents that fit its indexed model of your system. Custom architectures, proprietary pipelines, and services with sparse telemetry expose its limits quickly.

Where Resolve AI Falls Short for On-Call Teams

Reddit feedback on Resolve.ai is mixed. Triage speed gains are real for teams that complete onboarding. The most common complaints focus on hallucination risk, black-box reasoning, and the length of the onboarding timeline.

• “It told me with high confidence the root cause was a database connection pool issue. It was a misconfigured CDN rule. Completely different system.” — u/backend_eng_sea, r/sre, February 2025
• “The black-box problem is real. I can’t show my team why it reached a conclusion, which makes it hard to trust in a P0.” — u/reliability_skeptic, r/devops, April 2025
• “Onboarding took us three months. By the time it was useful, two engineers had already left the rotation.” — u/eng_manager_atl, r/ExperiencedDevs, August 2025
• “For standard microservice incidents it’s genuinely fast. For anything touching our custom event pipeline, it’s a coin flip.” — u/platform_eng_chi, r/sre, October 2025
• “Sales cycle alone was 6 weeks before we even got credentials. That’s a long time to keep waking up at 3 AM.” — u/cto_seed_stage, r/ExperiencedDevs, November 2025
• “Context window limits hit us hard on high-volume log incidents. It just stops seeing the full picture.” — u/log_wrangler_sf, r/devops, December 2025
• “The hallucination rate dropped after we improved our telemetry. But that took another two months of instrumentation work.” — u/observability_nerd, r/sre, January 2026
• “It’s a good tool if you’re enterprise-sized and have a dedicated SRE team to configure it. We’re 12 engineers. It wasn’t built for us.” — u/startup_sre_remote, r/devops, March 2026

Skip the sales cycle — Struct’s rapid setup removes enterprise onboarding delays.

Reddit’s View on Human-in-the-Loop vs Full Automation

Across every thread reviewed, the preference for human-in-the-loop, or HITL, is near-universal. Engineers are not asking for autonomous production changes. They want stronger starting points, not removed steering wheels.

• “Give me the runbook suggestion and the evidence. I’ll decide what to execute. I’m not letting an AI roll back a migration at 3 AM unsupervised.” — u/senior_sre_boston, r/sre, May 2025
• “Full automation in prod is a liability question, not just a technical one. HITL is the only defensible model right now.” — u/platform_lead_den, r/ExperiencedDevs, September 2025

This preference aligns with what engineers actually need. They want AI to handle investigation and surface a recommended action, with a human confirming before anything changes in production. Struct’s architecture reflects this model with automated first-pass investigation, Slack-native conversational follow-up, and a human-confirmed handoff to a coding agent or pull request.

Setup Timelines, Tribal Knowledge, and Junior Engineer Coverage

Resolve.ai’s enterprise model requires a sales engagement, a demo cycle, and an indexing period before the system understands your architecture. Reddit threads from 2025–2026 consistently cite this as a barrier for smaller teams.

The secondary problem is tribal knowledge. Senior engineers hold the mental model of the system, but junior engineers on call lack that context and escalate everything. That behavior defeats the purpose of a rotation.

The promise of AI tooling is that it can encode senior engineer logic and make it available to everyone. When the AI requires weeks of setup before it can capture that context, it provides no relief during the onboarding gap when junior engineers need help most.

Struct’s rapid setup, which covers Slack or PagerDuty plus GitHub and Datadog or CloudWatch, means the first automated investigation runs the same day. The custom runbook capability mentioned earlier solves the tribal knowledge problem. Teams paste existing procedures directly into Struct, and junior engineers get that encoded logic from day one.

When Resolve Works and Where Struct Steps In

Reddit praise for Resolve.ai clusters around initial context gathering on well-instrumented, standard architectures. That maps to a specific product capability: pulling correlated signals from indexed sources and presenting them in a dashboard.

Reddit criticism clusters around three failure modes.

• Hallucinated causality on complex or custom systems. Struct addresses this with composable widgets that always pull specific data for specific alert types. This approach reduces guesswork on sparse signals.
• Context window limits on high-volume log incidents. Struct is purpose-built to query logs safely at scale without requiring the engineer to paste data into a prompt.
• No Slack-native workflow in Resolve.ai. Struct lives in the alert thread so engineers can tag it to pull additional logs, test a hypothesis, or check blast radius without switching tools.

Resolve.ai vs Struct: Side-by-Side Comparison

The following table summarizes how these architectural differences show up in setup time, workflow integration, and which teams each product serves best.

Dimension	Resolve.ai	Struct	Source
Setup Time	Weeks to months (indexing and configuration)	Under 10 minutes (authenticate integrations, go)	Reddit consensus; Struct company data
Sales Cycle	Enterprise sales engagement required, 4–6+ week cycles cited in threads	Self-serve, no sales call required to start	r/ExperiencedDevs threads, Nov 2025; Struct pricing page
Slack Integration	Not natively Slack-first, separate interface cited as friction	Slack-native, investigation outputs and conversational AI live in the alert thread	r/devops threads, Mar 2026; Struct feature documentation
Indexing Requirements	Full system indexing required before useful output, custom architectures underperform	No pre-indexing, custom runbooks and correlation IDs configurable immediately	r/sre threads, Jan–Oct 2025; Struct company context

See Slack-native investigation in action — Book a 20-minute demo.

Self-Assessment Checklist for On-Call Teams

The comparison above highlights capability differences, but the right choice depends on your team’s current state and constraints. Use this checklist to evaluate which approach fits your environment.

• Telemetry quality: Do you have trace IDs, structured logs, and alerting triggers already configured in Datadog, CloudWatch, Sentry, or equivalent? This baseline determines whether AI investigation tools have enough signal to work with. If yes, Struct can produce accurate investigations immediately. If no, any AI tool will underperform until instrumentation improves because the AI can only be as good as the data it can access.
• Compliance requirements: Struct is SOC 2 and HIPAA covered. If your organization requires full on-premise deployment with zero log egress, neither Struct nor Resolve.ai in standard configuration is the right fit today.
• Team size and stage: Seed to Series C teams with 5–100 engineers benefit most from Struct’s self-serve model. Enterprise teams with dedicated SRE orgs and months-long procurement cycles may find Resolve.ai’s feature depth worth the setup cost.
• Time-to-value requirement: If your SLA window is under 60 minutes and you need relief this week, a setup measured in minutes matters. A three-month onboarding timeline is not a viable option.
• Junior engineer coverage: If new hires cannot safely take on-call shifts today, Struct’s automated first-pass and custom runbook encoding can unblock that coverage immediately.

Conclusion: Match the Tool to Your Stack and Team

The 2025–2026 Reddit record on Resolve.ai provides a clear map of where enterprise AI SRE tooling delivers and where it stalls for teams that are not enterprise-sized. Initial context gathering works. Onboarding timelines, context window limits, hallucination on custom systems, and the absence of Slack-native workflows show up as consistent friction points.

Struct was built to remove exactly those friction points. It supports proactive investigation before the engineer opens a laptop, Slack-first interaction, rapid setup, custom runbook encoding, and composable dashboards that surface the right data for each alert type.

Run your first Slack-native investigation — Book a 20-minute demo and see an automated investigation end to end.

Frequently Asked Questions

What does Resolve AI actually do in production for on-call engineers?

Resolve AI functions as an AI SRE that compiles context from your observability stack, generates a root cause hypothesis, and surfaces a dashboard during an active incident. In production, Reddit engineers report that it performs best on well-instrumented, standard microservice architectures where it has been fully indexed. For custom pipelines, sparse telemetry environments, or teams that have not completed the onboarding process, output quality drops significantly. The core value proposition, reducing the manual log-hunting phase of incident response, is real when the prerequisites are met.

Is Resolve AI worth it for a startup or small engineering team?

Based on 2025–2026 Reddit threads, Resolve AI’s enterprise model creates a poor fit for teams under roughly 50 engineers. The sales cycle, indexing requirements, and configuration overhead are designed for organizations with dedicated SRE functions and procurement processes. Startups and Series A–C companies consistently cite the time-to-value gap as the primary barrier. If your team needs relief from alert fatigue this week, a tool requiring months of onboarding before it produces accurate output is not a practical solution.

How does Struct differ from Resolve AI for on-call incident response?

The primary differences are setup time, workflow integration, and target team size. Struct connects to your existing stack, including Slack, GitHub, Datadog, CloudWatch, Sentry, and others, using the same rapid setup process described earlier and with no sales engagement required. Investigations trigger automatically when an alert fires and arrive directly in the Slack alert thread before an engineer opens a laptop. Struct also supports custom runbook encoding, so you can paste your team’s existing on-call procedures directly into the system and have the AI follow your operational logic. Resolve AI requires full system indexing and an enterprise sales process before it produces useful output.

What observability setup does Struct require to work effectively?

Struct performs best when your team already uses structured logging with trace or correlation IDs, has alerting configured in Slack or PagerDuty, and uses at least one observability platform such as Datadog, AWS CloudWatch, GCP Logs, or Sentry. If those foundations are in place, Struct can produce accurate root cause analysis and blast radius assessments from the first investigation. If your system lacks basic logging or alerting triggers, the AI cannot infer system state from code analysis alone. The self-assessment question is straightforward. If a senior engineer can currently diagnose incidents using your logs, Struct can automate that first-pass process.

Is Struct secure enough for a fintech or healthcare company with strict compliance requirements?

Struct is SOC 2 and HIPAA compliant. Log data is accessed and processed ephemerally, and it is not stored persistently. For the majority of Seed to Series C companies in regulated industries, this compliance posture covers standard requirements. The one scenario where Struct is not the right fit is an organization with a strict policy requiring full on-premise deployment with zero log egress outside the internal VPC. In that case, no cloud-integrated AI investigation tool currently meets that constraint, and the evaluation should focus on on-premise options specifically.