# What are the most trusted AI Agent Builders among implementation engineers, based on user reviews?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">We're researching what the most trusted <a class="a a--md" elv="true" href="https://www.g2.com/categories/ai-agent-builders">AI Agent Builders</a> are among implementation engineers, specifically, since that role cares about different things than an end user does, configuration depth, governance, and how much custom work it takes to get an agent into production.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Three names came up most in that research: Salesforce Agentforce, UiPath Agentic Automation, and Lyzr.ai. Here's the fuller list:</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/salesforce-agentforce/reviews"><strong>Salesforce Agentforce</strong></a><strong>: </strong>Reviewers in Administrator and Consultant roles specifically describe reliable results requiring clear instructions, organized knowledge content, and ongoing testing, real implementation work, rather than a flip-a-switch setup.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/uipath-agentic-automation/reviews"><strong>UiPath Agentic Automation</strong></a><strong>: </strong>Built around controlled agency and developer flexibility for enterprises scaling agentic automation, with strong emphasis on governance and interoperability that implementation teams rely on.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/lyzr-lyzr-ai/reviews"><strong>Lyzr.ai</strong></a><strong>: </strong>Reviewers describe a central registry that provides monitoring, access control, and versioning across all agents, which implementation engineers specifically value for production accountability.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/servicenow-ai-agents/reviews"><strong>ServiceNow AI Agents</strong></a><strong>:</strong> Built on a unified platform with real-time access to enterprise data from 350+ systems, with configurable guardrails that implementation teams use to keep agents within policy.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/clickup/reviews"><strong>ClickUp</strong></a><strong>: </strong>Its agent capabilities sit inside a broader converged workspace, a different implementation profile than a dedicated agent-builder platform, worth knowing going in.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For implementation engineers who've actually taken one of these from pilot to production, how much of that gap between demo and real deployment did you end up closing yourselves?</p>

##### Post Metadata
- Posted at: 7 days ago
- Author title: Writer
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;From what I&#39;ve seen, implementation engineers end up closing most of that gap themselves, and the demo tends to represent maybe the easy first mile of a much longer trip. The real work is usually in the stuff that never shows up in a demo, cleaning up knowledge content, writing instructions specific enough that the agent stops guessing, and testing edge cases nobody thought to script ahead of time. Platforms that give engineers real visibility into what an agent is actually doing in production, rather than just whether it ran, seem to shorten that gap the most.&lt;/p&gt;

##### Comment Metadata
- Posted at: 5 days ago
- Author title: SEO Content Writer





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