<div id="readability-page-1" class="page"><div><p><a href="https://www.lindy.ai/" target="_blank" rel="noopener noreferrer" data-external="true">Lindy</a> is an AI assistant whose primary objective is to save users' time. &nbsp;Lindy is described as “supremely reliable and attentive to detail.” We had an interview with its creator, <a href="https://twitter.com/Altimor" target="_blank" rel="noopener noreferrer" data-external="true">Flo Crivello</a>, about his methodology in developing Lindy AI and internal tooling for the agent.</p>
<h2 id="overall-approach-towards-building-agents">Overall approach towards building agents</h2>
<p>Flo comments on how they are building Lindy.</p>
<p>“We think of our approach as two halves. An AI agent needs:</p>
<ol>
<li>The correct collection of tools</li>
<li>To know how to utilize them."</li>
</ol>
<p>They are building a framework focused on the second point in particular - integrations aren't the big risk here.</p>
<p>Right now, the Lindy AI team is especially focused on techniques here that will get the agent to self-improve / learn from its own experience how to use its tools better and better.“</p>
<h2 id="main-use-cases-and-ideal-users">Main use cases and ideal users</h2>
<p>Lindy AI is a personal assistant for making a user's life more efficient. It can assist with all daily tasks, from managing user's schedule and composing emails to sending contracts, and more.</p>
<p>“Our ideal user is a senior manager in the technology space,” says Flo.</p>
<figure><figcaption>Examples of use cases. Source: Lindy AI landing page (https://www.lindy.ai/)</figcaption></figure>
<h2 id="reliability">Reliability</h2>
<p>Lindy has made great progress in reliability. When unsure about a task or when about to perform a high-stakes action, the agent asks the end user for confirmation.</p>
<p>"These confirmations become increasingly unnecessary as time goes by, both because we make Lindy smarter, and because she learns the user's preferences," explains Flo.</p>

<p>We asked Flo how they currently approach agent's debugging, monitoring, and tracing, what are the main struggles in this area, and how they are planning to solve them.</p>
<p>"We build a wide range of internal tools for example for tracing, and monitoring." shares Flo. "The agents' debugging in principle is similar to regular debugging: you examine the logs you have, try to reproduce them, then try different solutions until the bug is fixed. "</p>
<p>The Lindy AI team has developed all the agent tooling in-house.</p>
<p>"Our most important tool is probably the tracer, which shows step by step what the agent did exactly to fulfill a user query," says Flo. "It is similar to <a href="https://smith.langchain.com/" target="_blank" rel="noopener noreferrer" data-external="true">Langsmith</a>, but way better in our opinion."</p>
<figure><figcaption>Source: Lindy AI</figcaption></figure>
<p><strong>Lindy AI team also has tools for</strong>:</p>
<ul>
<li>Seeing the "lessons" that the agent is learning over time (the Memory in the screenshot above) and editing them</li>
<li>Reviewing the tools available, and editing the instructions about when and how to use each tool for the agent</li>
<li>Editing global rules or action-level rules</li>
<li>Monitoring benchmarks</li>
</ul>
<h2 id="other-challenges">Other challenges</h2>
<p>During the discussion, Flo mentioned additional problems that they are presently aiming to resolve, specifically fine-tuning and cognitive architecture.</p>
<p>"Right now, coming up with the right cognitive architecture is insanely challenging for us," says Flo, "but we wouldn’t outsource it, since we believe that’s our purpose. "</p>
<p>"Fine-tuning our model is also very painful, especially because we require a big model (40B+ parameters) and a big context window (8k+)," adds Flo. "I foresee that once we have that model, deploying it for inference at scale will be another significant challenge."</p></div></div>

E2B Blog & Customer Stories

Vaclav Mlejnsky

Lindy is an AI assistant whose objective is to save users' time. ” We asked its creator, Flo Crivello, a few questions about building agents.

Building tools for LLM agents with Flo Crivello - CEO at Lindy AI

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