---
title: "Inngest - AI in Production: The 2026 Benchmark Report"
sha256: 385d8af2f20edf8f9fc679ebcd41e8ee1d6272fad40726f007fc04a59d62fc73
source: newsletter
source_url: "https://www.inngest.com/content/ai-in-production-report-2026"
tags: [inngest]
url: "https://www.inngest.com/content/ai-in-production-report-2026"
fetcher: jina
review_value: 7
review_confidence: 9
review_recommendation: worth-reading
review_stars: 3
ingested: 2026-05-14
created: 2026-05-14
updated: 2026-05-14
---
Markdown Content:
AI in Production: The 2026 Benchmark Report
Key findings — preview
## What 130 engineers told us about running AI in production.
A preview of what's inside. We wanted to know what's causing failures, and which infrastructure choices — across orchestration, observability, evals, and agent frameworks — actually reduce the burden of reliability. Download our full report to go deeper with team-size breakouts, complete charts, and the statistical significance behind every finding.
*   01 — The confidence paradox
### Only 19% of teams running AI in production are very confident their stack can handle 2–3x scale.
At organizations with 500+ engineers and significantly more resources, that number drops to 0%. Our report explains why.
— 01 0 Engineers surveyed
— 02 0%Had incidents in last 90 days
— 03 0%Had confidence at scale 
*   02 — The observability gap
### Observability is the #1 unsolved problem engineers named in the survey.
Even respondents using a mix of third-party and homegrown solutions are spending hours diagnosing failures. The report shows which observability approaches actually correlate with faster recovery.
Observability & debugging
Agent state & durability
Non-determinism
Scale, infra & cost
Tooling fragmentation
Evals & output quality
Testing & integration
Unclear / N/A Up to 10 n=47
9 12 2 7 3 4 3 7  11–50 n=40
8 7 4 5 6 3 2 5  51–500 n=27
10 3 6 3 3 1 1  500+n=16
4 2 7 2 1   
19% of responses name observability as the core unsolved problem — the highest of any theme, and equal across AI (18%) and non-AI (21%) teams. 
*   03 — The reliability tax
### 20% of AI teams spend up to half their engineering time on reliability work.
That's twice the rate of non-AI teams. And for most, the burden is growing. Our report identifies which orchestration approaches correlate with lower — and higher — reliability burden.
AI in production
No AI in production  
AI teams are twice as likely to be in the 26–50% band (20% vs. 10%). Non-AI teams are more likely to be lean — 43% spending less than 10% vs. 32% of AI teams. 
*   04 — What separates confident teams
### Three infrastructure layers separate confident AI teams from the rest.
What separates the most confident AI teams isn't bigger budgets or bigger teams — it's tighter integration between three infrastructure layers: orchestration that persists state and handles failures, observability that lives inside the workflow, and evals connected to where things actually break. When those layers share context, confidence follows.
Strongest positive combinations
Durable execution + using evals + report declining reliability overhead Durable execution + using orchestration platform for observability + report declining reliability overhead Durable execution + using evals Using evals + report under an hour to debug Durable execution + report under an hour to debug Using orchestration platform for observability + report under an hour to debug   
The full report breaks down these combinations by team size with complete chart data and statistical significance.
Free PDF
## Get the full report.
Charts, breakouts by team size, and the patterns that predict scaling confidence — free PDF, instant access.
Methodology
## How we ran this survey.
130 backend, full-stack, and AI engineers and engineering leaders across companies of every size — from solopreneurs to organizations with 1,000+ engineers. All respondents were required to be running asynchronous workflows in production.
The survey covered orchestration, observability, evals, agent frameworks, and scaling confidence.