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---

---
title: >-
  How Evergreen.ai uses Pydantic Logfire and Evals to build observable,
  production-grade AI agents for financial services
company: Evergreen.ai
date: '2026-07-23'
summary: >-
  Evergreen.ai uses Pydantic Logfire to monitor AI agent runs, MCP tool
  performance, and guardrail events in real time. Combined with Pydantic Evals
  for offline and online evaluation, the team debugs agent issues in seconds and
  continuously measure quality across production conversations.
industries:
  - Financial Services
  - AI Agents
industryGroups:
  - Financial Services
  - AI & Data
productsUsed:
  - Pydantic Logfire
  - Pydantic Evals
canonical: 'https://pydantic.dev/case-studies/evergreenai'
---

> Markdown version of [How Evergreen.ai uses Pydantic Logfire and Evals to build observable, production-grade AI agents for financial services](https://pydantic.dev/case-studies/evergreenai) — the canonical HTML page.
>
> Evergreen.ai · 2026-07-23 · Financial Services, AI Agents
>
> "Having it built primarily off a first-class observability platform is the big thing that sold me as we're not running just LLM agents. We want to see observability on your API endpoints and all that other stuff too." — Bryan Godwin
>
> All case studies: [/case-studies.md](https://pydantic.dev/case-studies.md) · Site index: [/llms.txt](https://pydantic.dev/llms.txt)

---

## About Evergreen.ai

[Evergreen.ai](https://evergreen.ai) builds AI-powered agents for a financial planning and guidance app that handles user questions about financial topics. Their system relies on dynamic tooling, MCP servers, and guardrails for compliance, designed to help responses meet the standards required in regulated financial services.

The platform routes queries through multiple agent paths: a fast agent for straightforward questions and a deep agent for complex financial analysis. Both the engineering and product teams monitor agent behavior, with product managers reviewing guardrail blocks, flagging false positives, and tracking conversation quality alongside engineers.

## The challenge: Production AI agents in financial services demand reliability and visibility

Running AI agents in production for financial services is uniquely difficult. Agents are inherently dynamic and brittle: tools fail, models return unexpected output, guardrails misfire, and MCP servers introduce new failure modes with every integration. Without real-time AI agent observability, the team had no way to quickly trace what went wrong in a specific conversation or measure whether agent quality was improving over time.

Purpose-built LLM tools offered narrow visibility into model calls, but Evergreen.ai needed full-stack observability: tracing not just LLM inference, but API endpoints, MCP tool execution, guardrail decisions, and the complete agent workflow. The team needed to debug individual conversations, monitor AI guardrails and MCP tool health, and continuously evaluate agent quality, all in one platform.

> "If a user is reporting an error, it's really easy to come in and go dive into it and figure out what's going on."
>
> — Bryan Godwin, Evergreen.ai

## The solution: Logfire for end-to-end agent observability

### Live tracing with correlated spans

Evergreen.ai adopted [Pydantic Logfire](https://pydantic.dev/logfire) for its live tracing and custom dashboard capabilities. The team uses live mode with correlated traces to drill into agent runs, seeing exactly where things slow down or where tools fail, whether it's an MCP server returning oversized responses or a model taking too long to generate output.

> "The live view is great. Having general observability with the correlated traces and the ability to drill in has been working really well. Seeing where things are slowing down, when tools are failing is very valuable."
>
> — Bryan Godwin, Evergreen.ai

### Custom dashboards for agent operations

Beyond live tracing, the team built dashboards tailored to their specific needs:

- **Agent runs & errors**: tracking runs with user ID and thread ID to trace individual conversations back to their root cause, with breakdowns by agent route (fast agent vs. deep agent)
- **MCP tool monitoring**: tool usage, failures, duration, and response length, flagging broken tools that return oversized responses
- **Guardrail events**: tracking blocks, notifications, and pass-throughs to spot false positives before they affect users
- **Model benchmarking**: tokens-per-second across different models and providers to identify inference bottlenecks


### Cross-team visibility

These dashboards are for engineers and the product team, as they're useful to monitor agent behavior, flag issues, and review guardrail decisions. The functionality gives non-technical stakeholders a window into how AI agents are performing in production.

> "These dashboards have been helpful for myself and even product to just be able to see what's coming through."
>
> — Bryan Godwin, Evergreen.ai

### Offline evals for CI/CD quality gates

As their observability matured, Evergreen.ai expanded into [Pydantic Evals](https://logfire.pydantic.dev/docs/guides/web-ui/evals/?utm_source=evergreenai_casestudy) for continuous quality measurement. The team runs offline evals against fixed datasets in CI/CD, covering:

- **Guardrail testing**: validating that guardrails correctly handle all three states: pass-through, notify, or block
- **RAG validation**: testing that knowledge base retrieval and summarization produce accurate, expected answers
- **Case comparison**: side-by-side output analysis across prompt and model changes to catch regressions

### Online evals for production quality

What makes Evergreen.ai's approach powerful is that the same LLM judges used in offline evaluation are applied to production conversations. The team processes real conversation data and runs the same compliance and quality evaluators against it, evaluating every single conversation, not only samples.

> "The same evaluators we use with our actual eval dataset are now being applied against a real-time dataset."
>
> — Bryan Godwin, Evergreen.ai


## The results: Observable and trustworthy AI agents

Today, Evergreen.ai has built the observability and evaluation foundation their financial services AI agents demand:

- **Real-time debugging**: custom dashboards with user ID and thread ID let the team trace any reported issue to its root cause in seconds
- **Guardrail confidence**: continuous monitoring of block, notify, and pass-through events catches false positives before they affect users
- **MCP tool health**: tool failure rates, durations, and response sizes are tracked, with broken tools flagged immediately
- **100% conversation evaluation**: every production conversation is evaluated by the same LLM judges used in offline testing, ensuring consistent quality measurement
- **Product team access**: non-engineers use dashboards to monitor agent behavior and flag issues directly, closing the loop between product and engineering

> "Having it built primarily off a first-class observability platform is the big thing that sold me... you're not running just LLM agents. You want to see your observability on your API endpoints and all that other stuff too."
>
> — Bryan Godwin, Evergreen.ai


<br />

---

**Want to achieve similar observability for your AI agents?** [Get started with Pydantic Logfire](https://pydantic.dev/contact?utm_source=evergreenai_casestudy).

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