Quick overview

Agentic capabilities are still experimental for high-stakes use cases, but can deliver real value when deployed carefully inside structured, monitored workflows.

This guide shows when agents make sense, where they fail, and how to evaluate them in production so your team can add autonomy without sacrificing reliability.

Why this matters

There’s a lot of noise around agents right now, but real production use cases for agentic capabilities are still pretty rare. Due to lack of strategic partnerships and siloed initiatives, this year a MIT Research report found that around 95% of GenAI pilots still fail to reach production.

With this article, we’re cutting through the hype to answer the questions that actually matter for you:

• Do I even need agentic behavior in my workflow?
• Where can things go wrong?
• How can I reliably ship agentic capabilities?
• How do I know if my agent is actually working in prod?

But the real honest truth? Agents for for enterprise use cases is still limited and very experimental. If your use case involves high risk, needs to adhere to business rules, must adhere to regulatory compliance, and would benefit from deep business context, relying on fully autonomous agents is not advisable today.

That said, there is tangible value in deploying agentic capabilities today if done predictably and reliably. Organizations are actively deploying products with stepwise autonomous capabilities inside well defined and monitored workflows.

You can introduce agentic elements gradually without committing to full automation.

Do you need to build an AI agent?

Agents make sense when you’re handling a set of tasks that are valuable and complex, but cost of error is relatively low.

Think Coding or Search, where trial, error, and iteration are natural part of the process and you can introduce clear mechanisms (e.g. unit tests for coding) to help the model get to the right answer.

Coding is a natural use case for agents because of unit tests. You can edit the code, run the tests, and instantly see if it works. Sure, not every dev has perfect test coverage, but at least the option is there to validate and verify.

But, the reality is that not every project needs to be an autonomous AI agent.

Sometimes a good old cron job, script, or well-placed API call gets the job done without the overhead. The same rules from effective software engineering apply here: start as simple as you can, iterate fast, measure progress, then ship something that works.

That said, you can still introduce some agentic behavior in your workflow. Your models can use ‘tools’ or nested workflows that can be invoked to validate or enrich intermediate model outputs. This way you’re still orchestrating the environment, and releasing some low risk ‘control’ to your LLM — making your workflow more powerful, but manageable.

Most of the production-grade apps that we see in production operate this way.

Where things can go wrong

Here are a few common patterns we’ve noticed among those attempting to build agentic use cases, along with some thoughts on how to approach them:

Overcomplicating the workflow

Complex workflows with too many steps or tools can confuse the agent. Simplify where possible and break tasks down into smaller, manageable actions.

Skipping prompt engineering for your tools

Function calling helps LLM models collect the right information for upstream functions to execute. But unclear parameter definitions or poorly written descriptions can confuse any model (or developer for that matter). Don’t forget that ‘function calling’ is not a classical programming module, so you’ll still have to prompt it well so that the model understands how to apply that tool to specific actions.

Expecting the agent to have all the context

Even when you think you've provided clear instructions, the model can still behave unexpectedly — because it sees the world differently. Test your workflow from the model’s perspective and make the prompt, tools, and environment as clear as possible.

When Claude engineers tested OS World (Computer Mode), they often got results that didn’t match their instructions. To improve, they put themselves in the model’s shoes — closing their eyes for a full minute, blinking at the screen for just a second, and repeating. Then they asked: If you had to write Python code for this, what would you do? This approach helped them refine their prompts and tools for far better results.

Equally important, make sure your business domain experts have an active role in shaping and evaluating your model's outputs through prompting and evals. No model will have the full context on your use case the way a domain expert will.

Not handling edge cases

Agents can get stuck or fail when faced with scenarios outside the "happy path." You need to anticipate edge cases and guide the agent on how to handle them through clear instructions and fallback strategies.

This is where having built-in feedback loops, like unit tests in coding, becomes crucial.

How can I reliably ship agentic capabilities?

Without feedback during iteration, you’re just adding noise, not signal—and without the right signal, an agent can’t reliably converge on the right answer. Here are a few tips on how to evaluate your agentic workflows:

• Start small: Begin with well-defined, narrow tasks where feedback is easy to gather, then expand from there.
• Set Clear Goals: Define what success looks like for the agent (e.g., accuracy, response time, user satisfaction)
• Add validation steps: Incorporate checks like unit tests, assertions, or verification prompts to confirm outputs at each step.
• Evaluate often: Test the agent against known correct outputs or expected behaviors. Use metrics like precision, recall, or user feedback scores to measure performance.
• Use human-in-the-loop reviews: Regularly review agent outputs, especially early on, to improve prompts and tool usage.

Once your product is in production, the journey isn’t over—you’ll need to constantly measure and monitor performance.

So, how do you verify it’s doing the right things in production? Let’s take a look in the next section.

How to verify if my agent is performing well in production?

One thing we always say when building agents: Trust, but verify. Here are a few ways to verify your agent in production works as anticipated:

• Log everything: Track inputs, outputs, and decisions to catch patterns, errors, and edge cases early.
• Monitor outputs: Regularly review responses for accuracy, consistency, and areas that need improvement.
• Gather user feedback: Testing with predefined test cases can’t catch everything, so you’ll almost certainly encounter edge cases in production. Set up a feedback loop that captures user interactions and agent actions. This feedback can then be used to stress-test the agent with edge cases and complex scenarios, ensuring it remains reliable.

Importance of choosing the right tools

Selecting the right tools for agentic AI development is crucial for long-term success. You need tools that cover the whole stack—experimenting, testing, deploying, and keeping it all running smoothly—so your team isn’t stuck duct-taping solutions together.

The right setup means accelerating time-to-market, improving reliability, and minimizing operational risks.

But even the best tools are limited if they’re only built for for developers. AI development needs input from everyone—engineers orchestrating the logic, domain experts adding real-world context, and product teams ensuring everything aligns with business goals.

The tools you choose should adapt to each of these roles, making collaboration seamless and ensuring AI that actually works in the wild.

Build reliable agentic workflows with Vellum

To ship agentic workflows reliably, you need a flexible framework that lets you test, verify, and deploy with confidence.

Vellum was built from the ground up, based on feedback from thousands of engineers using it to productionize AI workflows. Companies like Redfin, Drata, and Headspace use Vellum to give their teams the tools they need to build and scale agentic workflows without the headache.

If you're looking for a framework that simplifies the process and helps your team follow best practices, book a call with one of our AI experts here.

Extra Resources

• 2025 Guide to AI Agent Workflows →
• How the best teams ship AI solutions →
• Ultimate LLM Agent Build Guide →
• Understanding agentic behavior in production →
• How Drata built an enterprise-grade AI solution →

FAQs

When does it make sense to implement AI into my team?

If you have complex, valuable tasks with low cost of error, like coding or research, where iteration and validation are easy.
If you have repetitive, low stakes tasks like data aggregation or formatting where saving time is the biggest advantage

Do I need a fully autonomous agents to get real value?

No, many production systems only use partial autonomy (e.g., function calls, nested workflows).

What’s the biggest risk of deploying AI agents?

Not using tools that give observability to debug AI agents pre-production. Tools like Vellum provide a full platform to build and test AI agents for reliability before deployment.

How do I validate agent outputs?

Use evaluation tools like Vellum that help introduce unit tests, assertions, or verification prompts at key steps to confirm correctness.

What’s the role of domain experts for agentic workflows?

They provide context no model can infer. Embedding SMEs in prompt design and evaluation is crucial.

How do I monitor agent performance in production?

Log all inputs/outputs, track edge cases, and set up feedback loops from real users.

Can agents handle compliance-sensitive workflows?

Not yet reliably. High-risk or regulatory contexts should stick to tightly controlled automation.

How can I validate AI agent use cases before development?

Start small with a narrow use case, test against clear metrics, and expand only when reliable.

What tools do I need to put AI agents in production?

A platform that supports experimentation, versioning, testing, and monitoring without patchwork integrations.

Why Vellum for agentic workflows?

It provides the all the critical infrastructure and tooling needed for observability, evaluation, workflow orchestration. This turns AI projects into reliable AI workflows in production.