# 10 Lessons learned when building Charlie

DevFeed: [10 Lessons learned when building Charlie](<https://devfeed.tech/articles/10-lessons-learned-when-building-charlie-40859.md>)

Original publisher: [Read original article](<https://mutto.fyi/posts/2026/04/charlie-lessons/>)

Published: 2026-04-05T00:00:00Z

Content type: article

Language: en

Sources: [Mutt0-ds Notes](<https://devfeed.tech/sources/mutt0-ds-notes.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Development](<https://devfeed.tech/topics/development.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Testing](<https://devfeed.tech/topics/testing.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [development](<https://devfeed.tech/tags/development.md>), [evals](<https://devfeed.tech/tags/evals.md>), [financial](<https://devfeed.tech/tags/financial.md>), [llm](<https://devfeed.tech/tags/llm.md>), [openai](<https://devfeed.tech/tags/openai.md>), [tdd](<https://devfeed.tech/tags/tdd.md>)

## AI overview

The author shares lessons from building Charlie, an AI agent designed for financial institutions. The excerpt emphasizes keeping agent systems simple, designing evaluation pipelines around real-world financial queries, and using flexible, provider-agnostic architecture. It argues that direct retrieval-to-explanation paths can be more robust and faster for many questions, while evaluations must reflect the system's actual context and edge cases.

## Source excerpt

Last week, we launched Charlie, our first AI Agent, specifically designed and tailored for financial institutions. While designing and...