The Three Pillars of Trust for AI in Software Development

Here's the truth about AI agents being used in software development today: everyone's beginning to use them, but few really trust them.

For platform teams, this trust gap represents both a challenge and a massive opportunity. Developers need a few key things to trust AI agents enough to rely on them for important work, and platform engineers are uniquely positioned to be the catalyst that makes it happen. This post shows you how to build that trust and accelerate AI adoption across your organization.

The Trust Paradox in AI-assisted Software Development

The data tells an interesting story. 62% of developers are now using AI tools (up from 44% last year), but favorability actually dropped from 77% to 72%.

This is the trust paradox of AI adoption. Usage is climbing while confidence is declining. Developers are adopting AI tools faster than ever, but they're also becoming more skeptical about the results.

Why? Because the current AI tools create a "trust but verify" problem that doesn't scale. They're great at generating code that looks smart, but they leave you doing all the validation work. You get code snippets without knowing if they actually work, if they handle edge cases properly, or will break when they hit real production constraints.

This creates a confidence erosion cycle where AI generates promising code, you find issues during manual testing, and you trust the agent less next time. Instead of building confidence through proven results, current tools are making developers second-guess what they produce.

Meanwhile, conversations are leaning into which LLM to use and how to craft the perfect prompt, but nobody's talking about giving AI agents the production-like environments they need to generate real results. To understand why this matters so much, let's first look at common friction points developers are experiencing.

Why Current AI Tools Aren't Meeting Developer’s Needs

Developers are excited about AI, but they're also getting burned by it. Here's a few hurdles they are encountering.

The "Code Suggestion Trap"

Most AI tools today are basically fancy autocomplete. They spit out code snippets, maybe even entire functions, then basically say "good luck!" and leave you to figure out the rest.

Now you're stuck doing all the verification work. Does this code actually work? Has it been tested? Will it break in production? Does it handle edge cases? Is it secure? You end up spending just as much time checking the agents homework as you saved by using it in the first place.

The promise was increased productivity, but the reality is increased validation overhead.

The Local Development Wall

The next hurdle: AI agents running on your laptop are basically flying blind. They don't know about your internal APIs, your database schemas, your microservices, or any of the custom network policies that make your app actually work.

So the agent generates code that looks great locally but fails the moment it touches your real infrastructure. It's being put in a situation to make assumptions about your application that just aren't true.

The Security and Governance Gap

Enterprise teams have additional concerns beyond just functionality. How do you audit what an AI agent did? How do you ensure it's not exposing sensitive data or violating compliance requirements? Most AI experiments are operating as black boxes without the enterprise-grade controls that organizations need.

When developers install AI tools on their laptops or use cloud-based services, there's often no visibility into what data the agent accessed, what actions it took, or whether it followed your security policies. For regulated industries or security-conscious organizations, this lack of governance makes AI adoption a non-starter for both adoption and trust.

The Platform Engineering Opportunity

Developers are in experimentation mode with AI tools. Some are going out of bounds because they don’t know they have better options. They haven’t been trained or given the best practices for your organization. Sound familiar? It's the same pattern we've seen with cloud adoption where developers find tools that make them more productive, and if platform teams don't provide a path, they'll make their own.

The difference is, with AI agents, the stakes are higher. We're talking about internal code and secrets that have a high potential to be shared with external LLM providers. That’s.. not ideal.

But here's your chance. This isn't your first rodeo. The teams that get ahead of developer adoption by building better alternatives, or golden paths if you will, they win.

Right now, every developer is basically running their own AI solution with zero governance, zero observability, and zero standardization. That's a problem you can solve.

Three Essential Pillars for Trustworthy AI in Software Development

Let's talk about what developers actually need to trust AI agents. It comes down to three things, and they're pretty simple when you think about it.

Empowerment ("I can describe what I want, and it actually happens")

This is about going way beyond code suggestions. Developers need AI agents that can take a prompt like "add authentication to this API" and actually deliver a working, deployed, testable feature.

Not just code that might work. Not just suggestions you have to implement yourself. The goal should be a real endpoint you can reference, with proper database connections, that handles edge cases, and that you can immediately test and validate.

When an agent has access to your actual infrastructure (your databases, your APIs, your deployment tools), it stops making wild assumptions and starts making informed decisions. It knows your database schema, understands your API contracts, and can work within your actual constraints to produce trustworthy work like a real member of your team.

The empowerment comes from seeing results, not just savvy code. Click a link, test an endpoint, see it working. That's trust.

Safety ("I can experiment without breaking anything")

Safety isn't just about preventing disasters (though that's important). It's about psychological safety - knowing you can let an AI agent loose on a complex task without worrying it'll take down production, expose sensitive data or mess up your local environment.

This is where we have strong opinions and where ephemeral environments become your best friend. Every AI agent needs its own completely isolated environment + your infrastructure. There should be no configuration drift, no weird state from previous runs, no chance of stepping on other people's (or agents) work.

Safety can't be an afterthought. Your governance, security policies, and access controls need to be baked right into these environments. The agent operates within the same boundaries you'd set for a new team member with appropriate access, proper monitoring, full audit trails.

Unblocked ("I don't have to configure anything")

This is where platform comes in. Remember the difference between tools and workflows that get utilized and those that get abandoned often comes down to friction. The same is true for AI adoption.

Developers shouldn't have to become infrastructure experts to try AI workflows. The cognitive overhead of setup, configuration, and troubleshooting kills experimentation before it starts.

The most successful AI implementations remove barriers rather than adding them. When developers can focus on the problem they're trying to solve instead of wrestling with tooling, that's when you see real adoption and genuine productivity gains.

This isn't just about convenience. It's about enabling the kind of rapid experimentation that builds confidence in AI tools. When trying something new is easy, developers will actually try it. When it's complicated, they'll stick with what they know.

When you nail this, developers stop thinking about the infrastructure and start thinking about the problems they want to solve. That's when AI adoption can really take off.

The Infrastructure Gap Everyone's Missing

Building trust with agentic development requires a few things working together in tandem. You need solid models, great prompting, and the right infrastructure. Most teams are getting really good at the first two. They've figured out which LLMs work best for their use cases and how to write effective prompts.

But they're missing the third piece that makes realistic output possible.

When you give a capable AI model good prompts AND production-like context to work within, that's when you see the real breakthrough in trust and reliability.

Why Application Context Changes Everything

Think about how modern applications actually work. It's not just code anymore. It's code, configuration, secrets, infrastructure, network policies, deployment workflows, monitoring, logging, and the fifteen other things that make your app actually run in production.

When an AI agent understands this full context, it generates fundamentally different output. Instead of creating a REST API that works in isolation, it builds one that properly integrates with your auth service, follows your database conventions, handles your error patterns, and deploys using your actual CI/CD pipeline.

The validation happens automatically because the agent is working in an environment that mirrors production. When it says "this works," you can actually trust that because it's been tested against real constraints.

And ultimately, trust builds when developers see working systems, not just clever code. An agent that can show a functioning output demonstrates competence in a way that code suggestions never could.

Your Strategic Role as a Platform Team

This is where platform teams become the heroes of AI adoption.

Your role is expanding from infrastructure automation to AI experience enablement. You're creating the foundation that lets developers confidently delegate complex work to AI agents. You're building on the platform you already have and adding a new dimension that makes AI agents actually useful and safe for your teams.

The magic happens when you can abstract away all the complexity of AI infrastructure while maintaining the control and governance your organization needs. Developers get the simplicity of "just works" environments, while you ensure security policies, compliance requirements, and operational standards are automatically enforced.

This is about enabling developer velocity without sacrificing governance. When you get this balance right, AI adoption accelerates naturally because developers trust the guardrails you've built.

The Network Effect of Adoption

The beautiful thing about getting this right is how success breeds more success. When some developers start having positive experiences with AI agents in proper environments, word spreads quickly. Other developers see working demos, successful deployments, and real productivity gains.

This creates a virtuous cycle. More developers want to try AI agents, which leads to more success stories, which drives even broader adoption. Instead of fighting skepticism and resistance, you're building momentum across the organization.

Plus, you get the visibility and control that's been missing from the current chaos of individual AI tool adoption. Instead of wondering what developers are doing with AI, you have observability, governance, and standardization built in.

Building this infrastructure foundation isn't just a technical challenge. It's a strategic opportunity that will define how your organization adopts AI.

Why This Matters More Than You Think

The teams that solve AI infrastructure first are going to have a massive advantage. Not just in productivity, but in talent attraction, developer satisfaction, and business outcomes.

You're Building the Future of Work

When developers trust (and enjoy) working with AI agents because they operate in properly governed, production-like environments, adoption stops being cautious and starts being aggressive. Teams begin tackling bigger problems, automating more complex workflows, and shipping faster than they ever thought possible.

Think bigger. You're not just enabling current AI capabilities. You're building the foundation for what's coming next.

Single agents are just the beginning. The real transformation happens when teams can orchestrate multiple specialized agents working together on complex, multi-step workflows. The infrastructure patterns you establish now (ephemeral environments, policy enforcement, observability) become the foundation for enterprise-scale AI orchestration.

Platform Teams as AI Enablers

This positions platform teams at the center of organizational AI transformation. You become the team that makes ambitious AI projects possible by providing the infrastructure foundation that developers can build on confidently.

Developers start leveraging platform capabilities as competitive advantages rather than working around infrastructure limitations. Leadership sees platform investment as directly driving AI transformation rather than just keeping the lights on.

The Bottom Line

Trust in using AI agents in development lifecycle comes from having all the pieces working together. Great models and prompts get you started, but the infrastructure foundation is what makes developers confident enough to actually rely on AI for important work.

Get the infrastructure right, and AI adoption accelerates across your organization. Teams become more ambitious, developers become more confident, and platform teams become the heroes of AI-powered transformation.

The transformation is already happening. The only question is whether you'll lead it or spend the next few years trying to catch up.

Ramiro BerrellezaCEO & Co-founder View all posts