Five lessons from Google Cloud Consulting for moving agents into production

Vibe coding has shown us how quickly you can get an agent up and running. But moving that agent into production is a different story.

As executive teams prepare their end-of-year performance reports, proving what AI has actually delivered for the business is an immediate priority.

Google Cloud Consulting's Delta team has partnered with Fortune 1000 companies around the world. We’ve seen firsthand what separates successful rollouts from stalled experiments, and today, we’re sharing five lessons from our work to help leaders move agents out of the lab and into production.

1. Define "production-ready" before writing code

Before jumping into code, define your expectations and map out what each milestone delivers:

• Proof of concept: Proves technical feasibility of a use case with a lightweight implementation, using public, non-sensitive data in a sandbox environment. In our engagements, this phase typically takes around six weeks, assuming the use case is already defined.
• Minimum viable product: Covers the happy path for core users and critical user journeys end to end, using development data in a lab environment. It may include user acceptance testing (UAT) and typically takes around 12 weeks.
• Production-ready: Passes everything that surrounds the agent code, including CI/CD automation, FinOps reviews, security reviews, and automated tests.

2. Prevent scope creep with clear KPIs

Because stakeholders may add requirements that push out your launch date, it’s important to control scope early to keep timelines and team effort in check:

• Start narrow: Restrict the agent to one or two high-value actions. Trying to do everything at once (summarize, update the CRM, and triage) is how scope creep begins.
• Objective KPIs: Replace vague goals like "helpful answers" with measurable metrics like task completion rate, hallucination rate, and latency ceilings.
• Phased rollout: Have the agent handle routine actions first, with human-in-the-loop verification for complex or sensitive tasks, before you expand toward full automation.

3. Give user testing the time it needs

User acceptance testing (UAT) is often underestimated, but it’s how you confirm that your agent meets the business requirements and expectations of the people who will use it.

UAT can lead to changes that affect the intelligent core of the solution, which may be discovered while testing edge-cases. Be sure to allow enough time for fixes, development, and re-testing.

Let’s look at a few ways you can plan around inevitabilities to keep your launch on track:

• Expect non-linear workflows: Keep in mind that feedback may send your team back to earlier stages to adjust prompts, integrations, or workflows.
• Account for testing time: When your testers are internal employees, they're stepping away from their day jobs to do so. Ensure you build that time into the project schedule.
• Protect your process: Define exactly where UAT sits in your path to production. When done right, UAT builds user trust without derailing your timeline.

4. Bring end users in early

One of the main reasons for low adoption is that employees worry the agent is there to replace them. However, in the projects we've delivered, agents take on the repetitive tasks so people can spend their time on work that needs judgment. This goes a long way toward getting your teams on board.

For example, we recently helped a customer on a process that used to take an entire quarter to complete, reducing it to just a few days. By including employees in our initial exploration and development, they helped us define a concrete way to improve their processes. The repetitive activities were supplemented by an AI system that was built with them, allowing teams to focus more on value-added tasks where their judgement and expertise were key.

Here are a few ways you can involve people from the start to give them a stake in the outcome:

• Include them during exploration and requirements gathering so the agent solves the problems they actually have.
• Keep the feedback loop open throughout development to test concepts, validate workflows, and build confidence in the tool.
• Identify team advocates who can help with input and testing during development and with change management and training at rollout.

5. Prove your agent's worth with evaluation

Enterprise adoption means proving that your agent is performing as expected. Set up ongoing testing and evaluation from day one, even if your company doesn't have formal automated testing for AI yet.

To build trust and prove value to your security, finance, and operations teams, focus on a few practical steps:

• Build the right workflows: Teams need evaluation, testing, and fallback scenarios in place before they'll approve a production workload. Evaluating an agent is more difficult than evaluating a traditional ML model. Plan for ongoing iteration: test the agent's performance against specific hypotheses, verify the results, and improve as you go.
• Validate total cost of ownership against ROI: Calculate the full cost of ownership, including compute, maintenance, and API fees, and compare it against projected savings or new revenue before you commit.
• Monitor performance: Set up real-time tracking and alerts for latency, infrastructure health, model drift, and cost anomalies so you catch issues before they reach end users.

Get started

If you're planning your first agent rollout or tightening up one that's already live, the Delta team can help you get to production faster. Reach out to us to get started.

You can also learn more about Google Cloud Consulting services to see how our engineering teams can help you modernize and secure your agent workflows.