The Execution Gap in Enterprise AI

Enterprises are investing more in AI than ever before. Budgets are growing, teams are experimenting aggressively, and automation is increasingly viewed as a core source of long-term advantage. In software development, the impact is already clear: coding agents have delivered billions in savings and eliminated thousands of hours of repetitive work.

The opportunity now is to extend this success beyond engineering into customer operations, finance, compliance, healthcare, and internal decision-making. Yet most enterprise AI agents never move beyond demos. Roughly 95% of AI pilots fail to deliver sustained, measurable impact, not due to lack of talent or ambition, but because production environments expose failure modes that controlled tests never reveal. With over 80% of enterprises already using AI in at least one function, the ability to move from experimentation to reliable deployment is becoming a structural differentiator.

The core challenge is trust. Teams lack confidence in how agents behave outside controlled settings, especially in edge cases, ambiguous scenarios, and policy-constrained decisions where enterprise risk is highest. Success is hard to define, harder to measure over time, and difficult to maintain as models and workflows change. Without clear visibility into agent behavior, costs, latency, and reliability compound uncertainty, slowing rollouts and keeping AI trapped in pilot mode.

From Prototypes to Production

The root of this hesitation often lies in the fundamental requirements gap between building for AI native startups versus enterprises. It is clear to developers that Generative AI enables new user experiences that can disrupt the industry, but how different customers scale varies. 

For the AI native developers, the requirements are focused on speed, quality, and  developer experience. This means day-0 access to the newest open models and the lowest possible latency is essential for keeping up with ever changing real-time requirements. It is about a frictionless path from a prototype to production scale where performance is optimized per token. Speed is the primary moat, and any friction in the model lifecycle is a threat to survival.

Enterprises now face a more decisive moment. AI is no longer experimental; it is actively reshaping products, workflows, and competitive positioning across industries. Large language models, multimodal systems, and autonomous agents are enabling capabilities such as enterprise search, long context summarization, and complex workflow orchestration that were impossible just months ago. The challenge is not simply adopting AI but owning it strategically.

For the enterprise, outputs must be accurate, domain specific, fully governed, and seamlessly integrated with product development. Achieving this requires a move beyond simple API calls toward an enterprise scale architecture that combines product model co-design with robust infrastructure, including AI Gateways and Model Lifecycle Management. This is powered by a continuous Run, Eval, Tune, Scale framework, ensuring models deliver measurable impact while evolving with the needs of the business. Ultimately, the goal is to scale without losing the keys to the kingdom.

Closing the Loop: Product-Model Co-Design and the Inference Engine

Building a durable agent requires more than just a smart prompt; it requires a data flywheel driven by product-model co-design. Inference is the critical point where AI actually enters real systems and workflows, turning a static model into a dynamic participant in the business. However, inference is only one part of the solution. To move from a demo to a wide scale deployment, AI must be fast, reliable, and equipped with the operational rigor that enterprises demand. Without strong inference infrastructure that provides cost visibility, monitoring, and constant optimization, AI remains a black box. 

At Fireworks, we believe that providing this foundation is what allows teams to transition from "testing" to "running." Your use case is unique: it is about creating a loop where your data informs the next round of tuning, and tuning leads to better performance, eventually making AI a seamless, high utility component of the enterprise stack.

Contextual Evaluations: The Missing Control Layer for Enterprise AI micro1 

Inference delivers outputs. Evaluation determines reliability.

For enterprise AI, evaluations are the missing layer between experimentation and trust. Contextual evaluations measure whether an agent behaves the way a human expert would within a real workflow. They help teams understand agent behavior before deployment and confidently expand usage once live.

Most evaluation approaches fall short because they rely on synthetic prompts, generic data, or binary pass/fail checks. These tests reward pattern matching rather than judgment and fail to capture nuance, incomplete information, or the edge cases that create real enterprise risk.

Contextual evaluations are grounded in real tasks, not artificial questions. At micro1, expert human judgment is applied to realistic scenarios to reveal how agents behave, where they fail, and why—providing clarity on production readiness before deployment.

Contextual evaluations also guide model selection by comparing models against criteria that matter in production:

• Alignment with human judgment
• Consistency across edge cases
• Policy and rule adherence
• Output quality and precision
• Latency, throughput, and tool correctness

When evaluations are grounded in real workflows, model selection becomes an engineering decision rather than a guess. Without contextual evaluations, enterprises cannot confidently determine whether an AI system is safe or reliable enough to run in production.

3D Optimization Frontier

To move AI agents into production, developers must bridge the gap between performance and measurement. Developers may start with a big, expensive model that works well, but it’s too slow and eats your margins. To make an agent deployable, you have to navigate the tradeoffs between three specific pillars: Quality, Speed, and Cost.

The trap most enterprises fall into is trying to optimize one without measuring the others. If you try to cut costs by switching to a smaller model or lower precision without a rigorous eval suite, you have no way of knowing if your quality just fell off a cliff.  Your evals serve as the guardrails for your inference. They give you the statistical confidence to shrink your models and speed up your response times without breaking the product. When you invest in both high performance inference and contextual evals, you stop guessing and start engineering. You turn the "vibe check" into a closed loop where production data constantly improves your testing, making your agents reliable enough to actually ship.

Conclusion

As enterprises pair strong inference infrastructure with contextual evaluation systems, AI shifts from experimental to dependable. Teams gain confidence in how agents behave, not just in ideal conditions, but across edge cases, policy constraints and real operational complexity. This confidence is what allows agents to move faster into production, expand into higher impact workflows, and remain reliable as environments, data, and models evolve. 

The bottleneck is not interest, talent, or budget. It is trust. And trust is built through reliable inference and evaluations grounded in expert human knowledge.