Presented by DigitalOcean
From refactoring codebases to debugging production code, AI agents are already proving their value. But scaling them in production remains the exception, not the rule.
In DigitalOcean’s 2026 Currents research report, based on a survey of more than 1,100 developers, CTOs, and founders, 67% of organizations using agents report productivity gains. Meanwhile, 60% of respondents say applications and agents represent the greatest long-term value in the AI stack. Yet, only 10% are scaling agents in production.
The top blocker? Forty-nine percent cite the high cost of inference. It’s not just the price of a single API call. It’s the compounding cost as agents chain tasks and run autonomously. Nearly half of respondents now spend 76–100% of their AI budget on inference alone. This is a problem DigitalOcean is working to solve. What’s needed is infrastructure designed around inference economics: predictable performance, cost control under load, and fewer moving parts. That’s how 2026 becomes the year agents graduate from pilot to product.
52% of companies are actively implementing AI solutions (including agents)
Just a year ago when we ran this survey, only 35% of respondents were actively implementing AI solutions — most were still in exploration mode or running their first projects. Now it’s 52%. The shift from “let’s see what this can do” to “let’s put this into production” is well underway.
There’s an agent boom underneath these numbers. 46% of those respondents are specifically deploying AI agents, autonomous systems that execute tasks on their own rather than wait for instructions at every step. OpenClaw (formerly Moltbot and Clawdbot) is one recent example, an open-source assistant that connects to messaging apps, browses the web, executes shell commands, and runs tasks autonomously.
Where are those agents going? Mostly into code and operations:
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54% said code generation and refactoring, making it the clear frontrunner
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49% are automating internal operations
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45% are building customer support and chatbots
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43% are focused on business logic and task orchestration
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41% are using agents for written content generation
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27% are pursuing marketing workflow automation
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21% are conducting data analysis
Developers are leading the charge here. For example, Y Combinator shared that a quarter of its Winter 2025 startups were building with codebases that are 95% AI-generated. Then there’s what Andrej Karpathy calls “vibe coding” — describing what you want in plain language and letting the AI write the code.
The tooling has split to match different workflows. Cursor bakes AI into a VS Code fork for inline edits and rapid iteration. Claude Code runs in the terminal for deeper work across entire repositories. But both have moved well beyond autocomplete. These tools now operate in agentic loops, reading files, running tests, identifying failures, and iterating until the build passes. You describe a feature. The agent implements it. Some sessions stretch for hours — no one at the keyboard.
But agents aren’t just for engineers. They’re making their way into marketing, customer success, and ops. We see this internally at DigitalOcean, too. Experimental showcases and hack days have surfaced demos of AI workflows to test ad copy at scale, personalize emails, and prioritize growth experiments.
67% of organizations using agents report measurable productivity improvements
The productivity question is the one everyone’s asking: are agents actually delivering results, or is this still hype? The data suggests the former. Overall, 67% of organizations using agents report measurable productivity improvements. And for some, the gains are substantial: 9% of respondents reported productivity increases of 75% or more.
When asked what outcomes they’ve observed from using AI agents:
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53% said productivity and time savings for employees
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44% reported the creation of new business capabilities
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32% noted a reduced need to hire additional staff
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27% saw measurable cost savings
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26% reported improved customer experience
Internal research at Anthropic explores what these technologies unlock: when the company studied how its own engineers use Claude Code, it found that more than a quarter of AI-assisted work consisted of tasks that simply wouldn’t have been done otherwise. That includes scaling projects and building internal tools. It also includes exploratory work that previously wasn’t worth the time investment — but now is.
What pushes those productivity numbers even higher? Agents are learning to work together. Google’s release of the Agent Development Kit as an open-source framework marked a shift from single-purpose agents to coordinated multi-agent systems that can discover one another, exchange information, and collaborate regardless of vendor or framework.
That said, 14% have yet to see a benefit, and 19% say it’s too early to measure. From what we’re seeing, 2025 was largely a year of prototyping and experimentation, with 2026 shaping up to be when more teams move agents into production.
60% bet on applications and agents as the biggest opportunity in AI
Budgets follow the results. AI remains an active area of investment for the vast majority of organizations: only 4% of respondents said they don’t expect to invest in AI over the next 12 months. And where organizations are seeing productivity gains, they’re doubling down — on the application layer, not foundational infrastructure.
When asked where respondents expect budget growth over the next 12 months, 37% pointed to applications and agents, more than double the share for infrastructure (14%) or platforms (17%). The long-term view is even stronger: 60% see applications and agents as the greatest opportunity in the AI stack, compared to just 19% for infrastructure.
Market data backs this up. According to one report, the application layer captured $19 billion in 2025 — more than half of all generative AI spending. Coding tools led at $4 billion, representing 55% of departmental AI spend and the single largest category across the entire stack. Organizations are betting that the application layer, where AI actually touches users and workflows, will matter more than the underlying components.
49% say the cost of running AI at scale is their top barrier to growth
Agents only work if you can run them. And right now, inference is the bottleneck. Unlike training, which is a fixed upfront investment to build the model, each prompt to an agent generates tokens that incur a cost. That cost compounds with every reasoning step, retry, and self-correction cycle. At scale, this turns inference into an operational expense that can exceed the original investment in the model itself.
When we asked respondents what limits their ability to scale AI, 49% identified the high cost of inference at scale as their top barrier. This tracks with where budgets are going: 44% of respondents now spend the majority of their AI budget (76-100%) on inference, not training.
But solving for inference shouldn’t fall on developers.
The complexity of optimizing GPU configurations, managing parallelization strategies, and fine-tuning model serving infrastructure is not the kind of work most teams should be doing themselves. That’s infrastructure-level complexity, and cloud providers need to absorb it.
At DigitalOcean, this is central to how we think about our Gradient™ AI Inference Cloud. We’re investing in inference optimization so that the teams we serve don’t have to. Character.ai is a good example: they came to us needing to lower inference costs without sacrificing performance or latency. By migrating to our inference cloud platform and working closely with our team and AMD, they doubled their production inference throughput and reduced their cost per token by 50%.
That kind of outcome is what becomes possible when the platform does the heavy lifting. As agents move from pilots to production, the companies that scale successfully will be the ones that aren’t stuck solving inference on their own.
Wade Wegner is Chief Ecosystem and Growth Officer at DigitalOcean.
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