# Dialing up determinism: building production-grade AI  | LinearB Blog

> Alex Salazar from Arcade reveals the four "demo killers" that prevent AI agents from reaching production and explains how embracing constraint and determinism is the key to building secure and reliable AI that goes beyond simple conversation.

_This is a markdown rendering of a live HTML page on linearb.io, generated for AI/LLM consumption — it is not a markdown-only site. To get the full HTML page instead, request this URL with an explicit `Accept: text/html` header (no wildcard, no markdown preference)._


```json
{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "https://linearb.io/"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Blog",
      "item": "https://linearb.io/blog"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "Dialing up determinism: building production-grade AI ",
      "item": "https://linearb.io/blog/ai-demo-is-a-lie"
    }
  ]
}
```

[Home](https://linearb.io/)

/

[Blog](https://linearb.io/blog)

/

Dialing up determinism: building production-grade AI 

# Dialing up determinism: building production-grade AI 

![Photo of Andrew Zigler](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Headshot3_d7231cbda7?_a=BAVMn6ID0)

By [Andrew Zigler](https://linearb.io/blog/ai-demo-is-a-lie#andrew-zigler)

|

September 19, 2025

![Blog_Dialing_Up_Determinism_2400x1256_e11437808c](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_Dialing_Up_Determinism_2400x1256_e11437808c?_a=BAVMn6ID0)

In the world of AI, demos are easy. They can also be a lie. A large language model's ability to generalize and improvise makes it simple to create a dazzling proof-of-concept, but as [Alex Salazar](https://linearb.io/dev-interrupted/podcast/ai-demo-is-a-lie), co-founder of Arcade, warns, "going from that easy demo to something production grade, it might be an entirely different product."

This is the central challenge facing engineering leaders today: bridging the massive chasm between a flashy demo and a secure, reliable, and cost-effective AI system that delivers consistent value. Salazar argues that the key is to intentionally constrain the AI's creative freedom—a strategy he calls "dialing up determinism."

This article explores Salazar's insights on the four critical blockers to production-grade AI, his "calculator" approach to solving them, and the agent-native paradigm that this new way of building enables.

## **The four production blockers**

According to Salazar, 90% of the work in building an AI product happens _after_ the initial demo is working. The [journey to production](https://linearb.io/blog/ai-in-software-development) is blocked by four critical challenges:

1. **Consistency and accuracy:** A production system needs to be trustworthy, performing correctly 80-90% of the time, not just in cherry-picked examples.
2. **Security and safety:** Real-world deployments must protect sensitive data. The lack of secure, reliable authentication is why a true "personal assistant agent" still doesn't exist.
3. **Token costs:** A demo that costs $50 to run can quickly scale to millions of dollars when deployed to thousands of users.
4. **Latency:** Slow AI responses kill the user experience, but making models more accurate often makes them slower.

## **The solution: dialing up determinism with the 'calculator' approach**

Salazar's breakthrough insight was to intentionally constrain the AI's decision space. He uses a brilliant metaphor to explain this: instead of giving the AI a blank slate, his team provides it with a very limited set of pre-approved actions. It's like handing the AI a calculator, but first removing its general-purpose buttons and replacing them with a few specific ones that represent the only valid choices. The AI can then only "pick from the buttons" you've given it, ensuring its actions are predictable and safe.

This "calculator" approach dramatically improves reliability. By forcing the model to choose from a discrete set of well-defined tools or actions - rather than generating open-ended responses - you dial up determinism. For security in particular, this means handling authentication _outside_ the model. The AI can call a secure tool, but it can't directly manipulate credentials. The security logic is "inside the tool call, inside the button."

To get started, Salazar offers two key pieces of advice: first, build a muscle of evaluations right out of the gate, because [you can't achieve consistency without rigorous testing](https://linearb.io/blog/how-to-build-a-robust-engineering-metrics-program). Second, "descope the living daylights out of your project" and focus on narrow workflows where modern APIs already exist.

## **Embracing the agent-native paradigm**

This deterministic approach enables a new way of building software. Traditional APIs are resource-based (Create, Read, Update, Delete), but AI agents are intention-based. An agent thinks, "I'm gonna reply to an email," an intention that might involve a hundred API calls or zero.

This requires a paradigm shift for developers. You can't just consume a generic API; you have to build custom tools grounded in the specific domain and use case of your agent. This is also the key to solving "compounding error rates"—the way a multi-step AI workflow can fail if any single step goes wrong. By replacing probabilistic AI steps with deterministic tool calls, you dramatically reduce the chance of failure.

Building this way [requires new skills](https://linearb.io/blog/the-rise-of-product-engineers-in-the-ai-driven-era). It's not enough to be good at using AI tools; you need "agent-native" engineers who understand this new world. The most effective teams, like Arcade's, create a "cross-pollination" of expertise, where agent experts, auth experts, and distributed systems experts learn from each other to solve these novel challenges.

## **From dazzling demos to durable value**

The path from a dazzling AI demo to a durable, production-grade system is paved with discipline. As Alex Salazar's insights reveal, the magic of generative AI is not enough. Real-world value is only created when that magic is contained, directed, and made predictable.

The "calculator" approach - dialing up determinism by giving AI a specific set of tools to choose from - is the key to bridging this gap. It's a trade-off: you sacrifice some of the model's free-form creativity in exchange for consistency, security, and reliability.

For leaders and developers entering this [new agent-native world](https://linearb.io/blog/how-agentic-ai-will-disrupt-your-software-delivery-lifecycle), the lesson is clear. The most successful AI products won't come from the teams that build the most impressive demos, but from those who have the discipline to engineer a production-ready reality.

To hear more from Alex Salazar on building production-grade AI, listen to him discuss these ideas in depth on the Dev Interrupted podcast.

## Improve developer productivity with LinearB

Find us on

[](https://www.linkedin.com/company/linearb)
[](https://devinterrupted.substack.com/)

![Headshot3_d7231cbda7](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Headshot3_d7231cbda7?_a=BAVMn6ID0)

## Andrew Zigler

Andrew Zigler is a GTM Engineer at LinearB and the host of Dev Interrupted, a twice-weekly podcast and newsletter where 40k+ builders decode the transition to AI-native development and agentic orchestration. A classicist by training with a degree from The University of Texas at Austin, Andrew spent his early career teaching in Japan before channeling his interdisciplinary instincts into the tech world. His polymath background informs everything he builds, from automated workflows to the stories he tells about the seismic shifts reshaping software creation.

### Connect with

[](https://www.linkedin.com/in/andrewzigler)
[](https://substack.com/@zigler)
[](https://x.com/andrewzigler)

## Your next read

[![Cover image for AI agents are killing the pull request and reinventing CI/CD](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_Post_Name_2400x1256_5_496cdfde2d?_a=BAVMn6ID0)](https://linearb.io/blog/circleci-rob-zuber-ai-agents-pull-request-cicd-sdlc)

AI

[AI agents are killing the pull request and reinventing CI/CD](https://linearb.io/blog/circleci-rob-zuber-ai-agents-pull-request-cicd-sdlc)

CircleCI CTO Rob Zuber explains why AI adoption is killing the pull request and forcing engineering teams to completely reimagine the software development...

[![Cover image for Rippling's employee graph turns HR data into an agentic AI platform](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_Post_Name_2400x1256_be5ec4183f?_a=BAVMn6ID0)](https://linearb.io/blog/rippling-albert-strasheim-employee-graph-agentic-ai)

AI

[Rippling's employee graph turns HR data into an agentic AI platform](https://linearb.io/blog/rippling-albert-strasheim-employee-graph-agentic-ai)

Rippling CTO Albert Strasheim explains why the employee graph serves as the essential context layer for enterprise agentic AI. Discover how unifying HR, IT,...

[![Cover image for Deterministic guardrails must evolve to control probabilistic AI systems](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_Post_Name_2400x1256_5_f5375661ec?_a=BAVMn6ID0)](https://linearb.io/blog/launchdarkly-cameron-etezadi-deterministic-guardrails-probabilistic-ai-darkfactory)

AI

[Deterministic guardrails must evolve to control probabilistic AI systems](https://linearb.io/blog/launchdarkly-cameron-etezadi-deterministic-guardrails-probabilistic-ai-darkfactory)

LaunchDarkly CTO Cameron Etezadi explains why traditional engineering pipelines are breaking down in a probabilistic, AI-driven world. Discover how to safely...

## Structured data

_Machine-readable metadata (JSON-LD) embedded in the page for search/AI context — not content rendered on the page itself._

```json
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "LinearB",
  "url": "https://linearb.io/",
  "logo": "https://assets.linearb.io/image/upload/v1715628027/logo-mark-lg.svg",
  "description": "LinearB is the engineering productivity platform that helps engineering leaders prove AI is improving throughput without sacrificing delivery confidence, flow efficiency, or developer experience.",
  "sameAs": [
    "https://www.linkedin.com/company/linearb"
  ],
  "award": [
    {
      "@type": "Award",
      "name": "LinearB is a Leader in the 2026 Gartner® Magic Quadrant™ for Developer Productivity Insight Platforms",
      "dateAwarded": "2026",
      "awardedBy": {
        "@type": "Organization",
        "name": "Gartner®"
      }
    },
    {
      "@type": "Award",
      "name": "Great Place to Work Certification",
      "dateAwarded": "2025-2027",
      "awardedBy": {
        "@type": "Organization",
        "name": "Great Place to Work"
      }
    },
    {
      "@type": "Award",
      "name": "America's Best Startup Employers 2025",
      "dateAwarded": "2025",
      "awardedBy": {
        "@type": "Organization",
        "name": "Forbes Magazine"
      }
    }
  ],
  "hasCertification": [
    {
      "@type": "Certification",
      "name": "SOC 1 Type 2"
    },
    {
      "@type": "Certification",
      "name": "SOC 2 Type 2"
    },
    {
      "@type": "Certification",
      "name": "GDPR Compliance certification"
    },
    {
      "@type": "Certification",
      "name": "ISO 27001"
    }
  ]
}
```

```json
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Dialing up determinism: building production-grade AI ",
  "url": "https://linearb.io/blog/ai-demo-is-a-lie",
  "author": {
    "@type": "Person",
    "name": "Andrew Zigler"
  },
  "datePublished": "2025-09-19T07:00:00.000Z",
  "dateModified": "2025-09-19T07:00:00.000Z",
  "image": "https://assets.linearb.io/image/upload/v1758310153/Blog_Dialing_Up_Determinism_2400x1256_e11437808c.png",
  "publisher": {
    "@type": "Organization",
    "name": "LinearB",
    "logo": "https://assets.linearb.io/image/upload/v1777485755/linearb-logo-2026.png"
  },
  "description": "Alex Salazar from Arcade reveals the four \"demo killers\" that prevent AI agents from reaching production and explains how embracing constraint and determinism is the key to building secure and reliable AI that goes beyond simple conversation."
}
```

## More on linearb.io

### Top navigation

- [Book a Demo](https://linearb.io/book-a-demo)
- [AI Code Reviews — Catch security risks, bugs, and spec mismatches](https://linearb.io/platform/ai-code-reviews)
- [AI & Productivity Insights — See how AI tools affect cycle time and delivery speed](https://linearb.io/platform/ai-developer-productivity-insights)
- [Measure AI Impact — Track AI adoption and tie it to delivery outcomes](https://linearb.io/use-case/measure-ai-impact)
- [MCP Server — Chat with your data to spot patterns and boost output](https://linearb.io/platform/mcp-server)
- [Resource Allocation — Cost initiatives and shape your investment strategy](https://linearb.io/platform/resource-allocation)
- [Cost Capitalization — Capitalize engineering costs with audit-ready reports](https://linearb.io/platform/cost-capitalization)
- [Dev Team Management — Set targets and tie throughput to business outcomes](https://linearb.io/platform/goals-and-reporting)
- [DevOps Workflow Automation — Policy-based PR routing, approvals, and tests](https://linearb.io/platform/ai-workflow-governance)
- [AI Powered Support — Unify AI and human code delivery in one clear view](https://linearb.io/use-case/ai-powered-support)
- [Optimization — Surface friction with feedback and MCP insights](https://linearb.io/platform/developer-experience)
- [Reporting — Spot what's working and what needs attention](https://linearb.io/use-case/measuring-developer-experience)
- [Surveys — Turn developer feedback into actionable signals](https://linearb.io/platform/developer-surveys)
- [Platform overview](https://linearb.io/platform/overview)
- [Watch now](https://linearb.io/event/engineering-productivity-gap)
- [Customers](https://linearb.io/customers)
- [Pricing](https://linearb.io/pricing)
- [Why choose LinearB — Explore your data. Measure performance. Act to improve it.](https://linearb.io/why-linearb)
- [APEX framework — The operating model for AI-era engineering teams](https://linearb.io/resources/apex-framework)
- [Anti-FAQ — The questions other vendors won't answer](https://linearb.io/why-linearb/anti-faq)
- [Security — Enterprise-grade compliance and zero code access](https://linearb.io/security)
- [Build vs. buy — The hidden cost of building it yourself](https://linearb.io/resources/build-vs-buy)
- [Dev Interrupted Podcast — Conversations with engineering leaders](https://linearb.io/dev-interrupted/podcasts)
- [Reports & Guides — Deep dives on productivity and delivery](https://linearb.io/resources)
- [Webinars — Expert sessions on productivity and AI](https://linearb.io/resources?category=workshops)
- [Metrics Benchmarks — See how your engineering org stacks up](https://linearb.io/resources/software-engineering-benchmarks-report)
- [Blog — Product updates and practical insights](https://linearb.io/blog)
- [Help Center — Documentation, setup, and support](https://linearb.helpdocs.io)
- [API Docs](https://docs.linearb.io/api-overview)
- [Status](https://www.linearbstatus.com/)
- [Integrations](https://linearb.io/integrations)
- [LinearB Library](https://linearb.io/library)
- [Engineering metrics](https://linearb.io/library/engineering-metrics)
- [Platform engineering](https://linearb.io/library/platform-engineering)
- [Engineering glossary](https://linearb.io/library/engineering-glossary)
- [Developer productivity](https://linearb.io/library/developer-productivity)
- [AI in software development](https://linearb.io/library/ai-in-software-development)
- [Engineering management](https://linearb.io/library/engineering-management)
- [Developer experience](https://linearb.io/library/developer-experience)
- [DevOps](https://linearb.io/library/devops)
- [Engineering operations and the context layer](https://linearb.io/library/engineering-operations)
- [Engineering efficiency](https://linearb.io/library/engineering-efficiency)
- [Software delivery](https://linearb.io/library/software-delivery)
- [Research and data](https://linearb.io/library/engineering-benchmarks-and-research)
- [LinearB is a Leader in the 2026 Gartner® Magic Quadrant™ for Developer Productivity Insight Platforms](https://linearb.io/resources/gartner-magic-quadrant-dpi-platforms-2026)
- [Sign in](https://app.linearb.io/login)
- [Enterprise](https://linearb.io/solutions/enterprise)
- [Contact](https://linearb.io/contact-us)
- [About us](https://linearb.io/about-us)
- [Careers](https://linearb.io/careers)
- [Service agreement](https://linearb.io/services-agreement)
- [Privacy policy](https://linearb.io/privacy-policy)
- [DPA](https://linearb.io/data-processing-agreement)
- [Security FAQ](https://linearb.io/security-faq)
- [Substack](https://devinterrupted.substack.com/)

### Footer

_Additional links from the site footer, not repeated from the top navigation above._

- [GitHub](https://github.com/linear-b)
- [LinkedIn](https://www.linkedin.com/company/linearb)
- [Twitter](https://twitter.com/LinearB_Inc)