By Cesar Miguelañez —

How Production AI Agents Work: Reliability & Practices

Discover how production AI agents operate, their reliability, and best practices based on recent empirical findings.

How Production AI Agents Work: Reliability & Practices

Artificial intelligence (AI) agents are becoming an integral part of production environments, transforming workflows across industries. From automating repetitive cognitive tasks to helping businesses scale operations efficiently, these agents are deployed to increase productivity while reducing manual effort. But behind the excitement lies a nuanced reality: building, deploying, and maintaining AI agents in production is far more structured and pragmatic than the open-ended autonomy some envision.

In this article, we delve into the findings of a recent study surveying 300 practitioners and conducting detailed interviews with a subset of them. The study sheds light on how production AI agents are built and the challenges organizations face in making them reliable. Whether you’re a product manager or a technical practitioner, these insights can help you ensure the success of your AI-powered initiatives.

Why Build Production AI Agents?

The primary motivation for creating AI agents is, unsurprisingly, productivity. Organizations want to free up their workforce from routine tasks and allow them to focus on higher-value activities. These agents are being deployed across a broad range of industries, including finance, banking, corporate services, and technology. This breadth demonstrates that AI agents are no longer confined to the tech world - they are impacting nearly every sector.

While the promise of efficiency is alluring, building AI agents comes with its own complexities, requiring organizations to carefully weigh trade-offs between functionality, reliability, and scalability.

Core Characteristics of Production AI Agents

To understand how AI agents are structured in production, let’s break down some key attributes identified in the study:

1. Who Are These Agents Built For?

  • The majority of AI agents are designed for human users - either internal employees or external customers.
  • Fully autonomous agents that interact with other software or agents remain a niche use case, reflecting the demand for human-centric applications.

2. Latency vs. Output Quality

  • Unlike consumer-facing applications where speed is critical, production AI agents prioritize high-quality outputs over low latency.
  • Most users are comfortable waiting a few minutes for accurate responses, underscoring the importance of delivering meaningful results.

3. Closed vs. Open-Source Models

  • Production deployments overwhelmingly favor closed-source models like ChatGPT, Claude, and Gemini due to their reliability and performance.
  • Open-source models, while popular in discussions, are primarily used for niche requirements such as privacy concerns or highly specialized tasks.

4. Prompt Engineering Practices

  • Prompt writing is largely manual, with teams often leveraging AI to refine prompts.
  • Surprisingly, automated prompt optimization tools like DSPy are underutilized, as practitioners prioritize transparency and control over their workflows.

The Agent Autonomy Debate: Workflows Dominate

A major point of contention in the AI community is whether agents should operate autonomously or follow structured workflows. The study revealed that structured, predefined workflows with bounded autonomy are the clear favorite in production environments.

  • Predictability: Workflows ensure agents operate within guardrails, minimizing the risk of unexpected behavior.
  • Ease of Evaluation: Structured workflows are easier to monitor and debug compared to fully autonomous systems.
  • Reliability Concerns: Most customers prefer agents that complete tasks in a limited number of steps (1–10), avoiding open-ended loops of decision-making.

This preference highlights an essential truth: businesses value reliability and control over the theoretical advantages of unfettered autonomy.

Frameworks vs. Custom Development

Another key debate centers around whether to build AI agents using established frameworks or to create custom solutions. The findings here present a mixed picture:

Framework Adoption:

  • Two-thirds of surveyed agents use frameworks, with LangChain leading the pack (25% market share) followed by others like LlamaIndex and Crew.
  • Frameworks are particularly useful for teams that value rapid deployment and ease of use.

Custom Development:

  • Interviews revealed that most detailed case studies (17 out of 20) involved teams rolling their own solutions.
  • Why? Custom development allows greater control and flexibility, avoiding the abstractions and limitations of frameworks.
  • Writing a simple agent loop with direct API calls to the underlying LLM is feasible for skilled teams, making this approach attractive for production-grade customization.

The Messy World of Agent Evaluation

Evaluating the performance of AI agents remains one of the most challenging aspects of deployment. The study uncovered several trends:

How Evaluation Is Done:

  • Baseline Comparisons: 40% of teams compare agent performance against baseline systems (e.g., older software or human execution).
  • Human-in-the-Loop Evaluations: Three-quarters of practitioners rely on manual evaluations by domain experts or operators to assess outputs.
  • LLM Judges: Some teams use language models as secondary evaluators, complementing human feedback.

The Problem of Missing Benchmarks:

  • A significant 60% of teams do not use predefined benchmarks, often because crafting meaningful baselines is difficult or infeasible for novel tasks.
  • Instead, many rely on qualitative feedback, user monitoring, and subjective assessments - what the speaker aptly referred to as "vibes."

Key Challenges: Reliability Takes Center Stage

Reliability emerged as the number one challenge facing production AI agents. This issue encompasses several factors:

What Does Reliability Mean?

  • Ensuring agents produce consistent, correct outputs.
  • Guaranteeing repeatability in performance across different contexts and inputs.

How Teams Address Reliability:

  • Constrained Autonomy: By limiting agents to structured workflows, developers reduce variability and risk.
  • Guardrails: Features like read-only modes and predefined steps ensure agents cannot cause harm, even in high-stakes scenarios.
  • Incremental Deployment: Teams test agents in controlled environments before scaling to full production.

Despite continued reliability issues, the pragmatic approach of constrained workflows ensures that AI agents can still deliver significant value in production.

Key Takeaways

  • AI agents are productivity tools: They are designed to save time and reduce cognitive load, especially for repetitive tasks.
  • Closed-source models dominate production: Most deployments favor platforms like ChatGPT and Claude for their reliability and ease of use.
  • Workflows over autonomy: Structured, bounded workflows are the norm, as they mitigate risks and ensure predictable behavior.
  • Custom development thrives: While frameworks like LangChain are popular, many teams prefer rolling their own solutions for greater control.
  • Evaluation remains a challenge: Human-in-the-loop evaluations and qualitative feedback are more common than formal benchmarks.
  • Reliability is a top concern: Guardrails, predefined steps, and constrained autonomy are key strategies to ensure consistent performance.
  • Latency is secondary: Production agents prioritize high-quality outputs over fast response times, with users willing to wait for better accuracy.

Actionable Insights for Teams:

  1. Focus on high-quality prompt engineering, leveraging AI for refinement but maintaining human oversight.
  2. Consider structured workflows as a starting point rather than aiming for fully autonomous agents.
  3. Evaluate whether frameworks fit your use case or if custom development gives you more control.
  4. Build robust evaluation systems, combining human feedback with automated tools to monitor performance.
  5. Address reliability early by implementing strong guardrails and testing in controlled environments.

Conclusion

The real-world deployment of AI agents is a story of pragmatism over perfection. Rather than chasing open-ended autonomy or cutting-edge frameworks, most organizations focus on simplifying workflows, ensuring reliability, and delivering tangible results. For teams building and maintaining AI-powered products, these insights highlight the importance of balancing innovation with practicality. By adopting structured approaches and prioritizing reliability, you can set your AI agents up for long-term success in production environments.

Source: "What Do Production AI Agents Actually Look Like? An Empirical Study" - Vivek Haldar, YouTube, Dec 8, 2025 - https://www.youtube.com/watch?v=OhSDYlL3ESw

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