Safe Migration at Scale

Migrating at Scale — No-Code Agent Platform Switches to Gemini Models in Under a Week

Business Context

A fast-growing no-code agent platform empowers teams to build and deploy intelligent AI agents through a visual interface—without writing code. The no-code agents automate multi-step tasks like scheduling, drafting content, or responding to user queries using LLMs under the hood.

As user demand scaled, the team sought to migrate to a new LLM provider to improve latency and reduce costs. But the switch proved more complex than anticipated: although API-compatible, the new model introduced behavioral changes that cascaded through agent logic. Although a large number of errors and issues were detected, the migration proved worth the effort in cost savings and latency.

For reference, the no-code platform handles over 100,000 completions per day, with average context windows exceeding 65,000 tokens and tool calls in over 80% of completions. To reduce costs and latency while improving performance, the team migrated from OpenAI’s models to the Gemini family of LLMs. The platform continues to grow rapidly month over month and will require many future model migrations and model introductions.

Challenges

What began as a routine model upgrade quickly created systemic issues in production:

• Function calls were malformed—LLMs stopped passing correct parameter structures to connected tools.

• Agent roles blurred, with assistants taking on planner logic and vice versa.

• Infinite loops emerged, breaking previously stable flows.

• Factual reliability degraded, resulting in hallucinated or outdated answers.

• Personalized instructions were dropped, especially those involving user-specific memory or constraints.

Despite passing regression tests, the new model broke critical user paths. The engineering team lacked granular tools to detect or understand these failures quickly.

Okareo’s Solution: Real-Time Insight, Actionable Debugging

To regain control of the migration, the team integrated Okareo’s platform. With Okareo, they were able to:

• Trap issues in real time — Okareo automatically flagged errors like invalid tool call structures, excessive agent loops, and dropped directives during live and simulated runs.

• Compare behaviors against baselines — Okareo surfaced how the new model’s outputs diverged from prior expectations, down to the role attribution and factual content level.

• Rapidly patch, retest, and confirm fixes — Using targeted evals, the team validated improvements on real-world scenarios without reintroducing regressions.

• Monitor post-launch with confidence — Okareo’s always-on behavioral tracking provided continuous assurance that the new model was stable and improving over time.

Key Areas Where Okareo Helped

Structured Evaluation and Baselines
Okareo let the team define key behaviors—like correct tool usage, adherence to roles, and factuality checks—and then compare the new model’s performance to their existing benchmarks. This gave immediate visibility into regression risks that standard tests missed.

Simulation and Scenario Coverage
By simulating real workflows and edge cases, Okareo exposed hidden bugs—such as prompt fragility and role confusion—that only manifested under specific input chains.

Real-Time Replay & Evaluation
The team used Okareo to record production traffic from the OpenAI-powered system and replay it on the Gemini models. This surfaced key failures—like incorrect function arguments, misused memory, or instruction skipping—before any users were affected.

Each mismatch triggered structured evaluation alerts, enabling prompt fixes to prompts or routing logic. This replay strategy became the backbone of the migration process, allowing safe iteration without slowing velocity.

Error Surfacing and Prompt Patching
Okareo automatically flagged regressions across thousands of completions, allowing the team to:

• Pinpoint hallucination spikes and response degradation

• Debug tool call misalignments across versions

• Adjust agent routing logic with immediate feedback

• Evaluate success using custom pass/fail metrics

The result: a faster stabilization period, even for complex agent chains.

Post-Migration Confidence
Once live on Gemini, Okareo’s always-on evaluation layer continued monitoring for role misattribution, factuality drift, and failed completions. Issues were caught early, long before users reported them.

Results

Reduced Migration Risk
The team identified and resolved critical issues within days—not weeks—avoiding the need to roll back. Okareo gave them the insight needed to understand why the model was failing and how to fix it.

Faster Time-to-Resolution
Rather than rewrite agents blindly, engineers had targeted evals and failure examples they could use to tune prompts and adapt roles quickly.

Increased Confidence and Agility
Okareo turned model migration into a repeatable process. With real-time visibility and pre-launch simulations, the team could adopt better models faster—without compromising user experience.

Conclusion

LLM migrations are rarely clean copy-pastes. For this no-code agent platform, Okareo delivered the tools needed to make the process safe, observable, and efficient. By proactively catching behavioral shifts and enabling rapid iteration, Okareo helped the team ship a major model upgrade—with zero disruption and full confidence.