This guide breaks down the top 13 AI agent builder platforms of 2026, how to evaluate them, and where each fits in enterprise adoption. We compared and evaluated these solutions to make it easy to find and evaluate the perfect AI agent builder platform for your enterprise.
Top 6 agent builder shortlist
If you only want the contenders that matter most for enterprise teams in 2026, here’s the shortlist:
Vellum: Best overall for enterprises with prompt based agent building, collaboration, evals, versioning, and observability built-in.
Vertex AI Agent Builder (Google Cloud): Best for GCP shops needing RAG, memory, and compliance.
LangChain: Best for developer-led teams that want maximum flexibility and ecosystem depth.
AutoGen: Best for multi-agent collaboration and autonomous workflows.
CrewAI: Best for “team of agents” setups with role specialization.
Dify: Best for quick, low-code prototyping and simple enterprise workflows.
I’ve only seen enterprises find org wide success with AI agents when enabled to maintain control and flexibility through the platform to keep agents adaptive to their solution. The right platform will exponentially cut the time to develop, build, and iterate AI agents for internal and external use, allowing AI initiatives to produce real value and ROI quickly.
With MIT finding that 95% of genAI pilots fail to reach production, the path forward in 2026 is choosing a platform that will be your strategic partner in AI agent building [1]. A deeply customizable platform that supports users by enabling easy building and collaborative environments is an ideal solution for this approach.
If you are looking to bring your enterprise into the modern AI world, choosing your AI agent builder solution is a pivotal step to either enable success, or if chosen poorly, become another failed initiative. We put this guide together to help you make sure the ladder doesn’t happen.
What Is an AI agent builder?
An AI agent builder is a platform or framework for designing, deploying, and managing AI agents. They are systems powered by LLMs that can reason, use tools, and act across workflows.
Here are top three platform functions to keep in mind as you evaluate AI agent builders:
Low-code & collaboration features: Makes it easy for non-technical teammates to sketch, test, and adjust workflows without needing to write full code.
Deep Developer functionality: Gives engineers the ability to extend, customize, and harden workflows with SDKs, custom nodes, and integrations.
Governance: Provides version control, permissions, audit logs, and monitoring so organizations can trust and scale their workflows safely.
Why use an AI agent builder?
Capgemini Research found that AI agents have the potential to generate $450 billion in economic value by 2028, yet 2025 showed only 2% of organizations have deployed AI agents at scale with only 12% at partial scale [2].
Enterprises leaving this massive potential on the table struggling to easily and reliably build AI agent that get into production without it breaking, drifting, or losing stakeholder trust. That’s where agent builders come in as the solution to securely build and put AI agents into reliable use.
They provide the scaffolding needed to turn promising pilots into reliable systems:
Speed up delivery: Templates, visual editors, and pre-built connectors let teams move from idea to working agent in days, not months.
Reduce risk: Built-in evaluations, monitoring, and version control mean you catch regressions before users do.
Enable collaboration: Non-technical teammates can shape workflows while engineers extend and harden them, all in the same environment.
Scale with confidence: Governance features like RBAC, audit logs, and environment separation make it possible to expand usage safely across departments.
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What makes an ideal AI agent builder?
The ideal platform will be unique to your business but should balance speed, reliability, and governance in a way that works for enterprise teams.
In practice, your ideal builder is one that lets your non-technical teams move fast without creating messes engineers later have to clean up, while giving engineering the depth they need to harden, monitor, and scale AI agents.
Based on what we’ve seen across the market, here’s what sets a true leader apart:
Bi-directional syncing: Visual interfaces for PMs and SMEs, plus SDKs/APIs for engineers.
Evaluation and versioning capabilities: Every release can be tested, compared, and rolled back safely.
End-to-end observability: Traces, dashboards, and logs that show how agents behave in production.
Governance at scale: RBAC, audit trails, and environment separation that meet enterprise compliance standards.
AI-native primitives: Retrieval, semantic routing, memory, and deep orchestration customizability.
Flexible deployment: Options for cloud, VPC, or on-prem, so sensitive data never leaves your control.
Healthy ecosystem: Connectors, integrations, and a vendor roadmap that signal long-term stability.
How to evaluate AI agent builder platforms?
Forget mindlessly clicking from agent builder site to site and comparing spec sheets. Here’s an evaluation framework that will ensure you make a sound, long-term choice tailored to your use case:
AI Agent Builder Evaluation Framework
Use this checklist to score each platform 1–5 and capture notes. It resizes to any screen and scrolls horizontally on small devices.
Score vendors on each dimension. 1 = weak fit, 5 = strong fit.
Evaluation Topics
Key Questions to Ask
Why It Matters
Score (1–5)
Notes
Total cost of ownership
What costs appear at scale (context, memory, tool calls)? Any limits on runs, users, or connectors?
Avoids tools that start cheap but get expensive as usage grows.
Time to value
How fast can a non-technical user ship a useful agent? How long to stable production?
Shortens pilot cycles and accelerates ROI.
Fit for your builders
Can PMs/SMEs build visually? Do engineers get SDKs, scripting, custom nodes, CI hooks?
Matches the platform to your actual team skills and workflow.
AI-native capabilities
Are retrieval, memory, semantic routing, tool use, and multi-agent orchestration first-class?
Determines whether it can power real agent use cases without brittle glue code.
Testing & versioning
Can you run evals, compare versions, promote safely, and roll back cleanly?
Prevents regressions and supports evidence-based releases.
Observability
Do you get traces, logs, and performance metrics at node, agent, and workflow levels?
Makes incidents diagnosable and improvements measurable.
Cloud/VPC/on-prem options? Private networking? Regional data residency?
Aligns with IT policies and data privacy constraints.
Performance & scalability
Latency benchmarks, throughput, concurrency limits, caching, cost controls at scale?
Ensures agents remain fast and affordable as adoption grows.
Change management
Reviews, approvals, release gates, and safe promotion across environments?
Prevents shadow workflows and keeps teams aligned.
Support & community
SLAs, live support, solution architects, active user/OSS community?
Determines how quickly you unblock issues and adopt best practices.
The top 13 AI agent builder platforms in 2026
1. Vellum AI
Quick Overview
Vellum AI is the enterprise AI-first agent builder that lets teams create production-ready agents and AI Apps using natural language prompts. Its prompt-to-agent workflow turns plain-English instructions into complete agents instantly, the visual builder makes refining logic easy for non-technical teams, and the Python/TypeScript SDK gives engineers full extensibility. With built-in evaluations, versioning, and observability, enterprises get both speed and rigor. Once agents are built, Vellum packages them into governed AI Apps so employees across the org can safely automate work without touching code.
Best For
Enterprises that want to safely enable employees to automate work with AI while giving engineering and compliance the governance, reliability, and deployment controls they require.
Pros
Fast and easy work automation with prompt based agent building
AI Apps that empower employees to run and reuse automations safely
Visual builder plus TypeScript/Python SDK for deep customization
Built-in evaluations, regression tests, and versioning
Full observability: traces, dashboards, performance metrics
Vellum is an enterprise grade AI workflow and agent builder platform that lets non-technical teammates and engineers co-build reliable, testable, observable AI agent that scale. If you care about moving from pilots to production without slowing collaboration, Vellum is the right choice.
AI Apps for safe, reusable deployment: Package agents into governed apps so employees across the org can run and reuse automations without touching code.
Built-in evaluations and versioning: Define eval sets, easily compare model and prompt variants, promote only what passes, and roll back safely.
End-to-end observability: Trace every run at the node and workflow level, track performance over time, and spot regressions before they hit users.
Collaborative agent building: Shared canvas with comments, role-based reviews and approvals, change history, and human-in-the-loop steps so PMs, SMEs, and engineers can co-build safely.
Developer depth when you need it: TypeScript/Python SDK, custom nodes, exportable code, and CI hooks to fit your existing tooling.
Governance ready: RBAC, environments, audit logs, and secrets management to satisfy security and compliance.
Flexible deployment: Run in cloud, VPC, or on-prem so data stays where it should.
When Vellum is the best fit
When enterprises want to enable all their employees to easily build agents that automate their work
Your team includes both technical and non-technical people who need to build and manage AI agents together without breaking reliability.
You plan to build AI agents that use retrieval, run across multiple steps, and need to be tracked and improved as they scale.
You want changes to be backed by testing and monitoring, so every release is based on data instead of guesswork.
How Vellum compares (at a glance)
vs Zapier / Make / Pabbly: Great for lightweight SaaS automations. Vellum is built for more comprehensive AI agent orchestration with evals, versioning, and observability at scale.
vs n8n / Pipedream: Solid for technical DIY and open-source flexibility. Vellum adds governance, collaboration, and monitoring so both engineers and non-technical teams can build together safely.
vs LangChain / AutoGen / CrewAI: Strong frameworks for developer-led customization and multi-agent research. Vellum matches their flexibility but layers in enterprise rigor—evals, tracing, and rollback—so agents don’t stall in pilot.
vs Vertex AI / Azure Copilot / AWS Bedrock: Ideal for cloud-locked enterprises. Vellum is cloud-agnostic, integrating with all major providers while adding observability, governance, and model flexibility.
vs Workato / Power Automate: Enterprise iPaaS leaders for app-to-app automation. Vellum is purpose-built for AI workflows and agents, enabling faster iteration on prompts, retrieval, and orchestration while still meeting enterprise controls.
vs StackAI / Tray.ai Good for compliance-heavy or niche verticals (safety, packaged AI, voice). Vellum provides broader orchestration plus built-in testing and monitoring for general enterprise adoption.
vs Flowise / Dify / LlamaIndex: Useful for quick prototyping or RAG-heavy tasks. Vellum delivers similar accessibility but with enterprise-grade versioning, evals, and deployment options.
What you can ship in the first 30 days
Week 1: Set up your first AI agent using templates; connect knowledge sources; define a small golden set for evals.
Week 2: Add semantic routing and tool use; wire human-in-the-loop approvals for sensitive actions; start tracing runs.
Week 3: Set up regression tests, CI integration, and multi-environment promotion; add dashboards for stakeholders.
Week 4: Expand coverage to a second use case (e.g., support macros → sales research), reuse components, and monitor cumulative impact.
Proof you can show stakeholders
Before/after evals: Demonstrate factuality and latency improvements with side-by-side runs.
Trace-driven reviews: Walk leaders through exactly what the workflow did and why.
Promotion history: Show that changes were tested and approved—not pushed blind.
Operational metrics: Volume handled, error rates, and time-to-resolution trends.
Ready to build AI agents at enterprise scale on Vellum?
Start free today and see how Vellum’s scalable infrastructure, built-in evaluations, and collaboration tools help you turn AI agents into production-grade systems org wide.
An AI agent builder lets you design, deploy, and monitor agents powered by LLMs without hand-coding all the orchestration yourself. You chain together reasoning, retrieval, tool use, and approvals in one place, usually with a visual or low-code interface plus an SDK. Strong platforms, like Vellum, also add evaluations, versioning, and observability so every change can be tested and safely promoted.
2) Why do enterprises need an AI agent builder in 2026?
Most enterprises are stuck in pilot hell because custom scripts and ad hoc frameworks are hard to govern, monitor, and scale. An AI agent builder gives you a shared, governed environment where PMs, engineers, and compliance can collaborate. Vellum does this by combining prompt-based agent building, AI Apps, evals, and observability so you can move from pilots to production without losing control.
3) Who inside the enterprise benefits most from these platforms?
Product, operations, support, and sales teams benefit because they can describe workflows in plain language and get working agents, instead of waiting on engineering queues. Engineering, data, and security teams benefit because they get SDKs, governance, and deployment controls instead of debugging one-off scripts. Vellum is designed to serve both sides: employees build and run AI Apps safely while engineering and compliance set the guardrails.
4) How do I choose the right AI agent builder for my org?
Start from your realities: cloud alignment, regulatory requirements, team skills, and the workflows you care about. If you need a balance of low-code collaboration, deep SDKs, built-in evals, and enterprise governance across clouds, Vellum is usually the strongest default. Tools like LangChain, AutoGen, or Vertex AI are better fits when you need pure code-first flexibility on a single stack and are willing to own more of the plumbing.
5) How quickly can teams see value with Vellum compared to other options?
With OSS or raw SDKs, teams often spend weeks just wiring logging, evals, and environments before real users see value. In Vellum, you can use prompt-to-agent building and templates to get a first agent live in days, then harden it with evals and observability as you go. That makes it much easier to show stakeholders real, measurable impact in the first 30 to 60 days.
6) How is Vellum different from open source frameworks like LangChain or AutoGen?
LangChain and AutoGen excel for developer-led experimentation and custom orchestration, but you own governance, monitoring, and release discipline yourself. Vellum gives you comparable flexibility via TypeScript/Python SDKs and custom nodes, but layers in enterprise features like RBAC, audit logs, eval suites, and environment-based promotion. This means your agents are easier to take from proof of concept to audited, production-ready systems.
7) How does Vellum compare to cloud-native builders like Vertex AI, Azure Copilot Studio, or AWS Bedrock AgentCore?
Cloud-native builders are strong if you want to stay deeply inside one ecosystem and you are comfortable with that vendor’s models and governance. Vellum is cloud-agnostic and multi-model, so you can mix providers while still getting unified evals, observability, and governance. Many enterprises use Vertex, Azure, or Bedrock for core infra, then layer Vellum on top as the agent-building and collaboration layer.
8) How is Vellum different from iPaaS and automation tools like Workato, Zapier, or Make?
Zapier and Make are ideal for simple SaaS automations, and Workato is great for broad enterprise integration. They are not built primarily for AI-native orchestration, evals, or prompt and model lifecycle management. Vellum focuses on AI agents: prompt-to-agent building, retrieval, routing, evaluations, and observability, while still integrating cleanly with your existing iPaaS or automation stack.
9) What does a good first 30 days on Vellum look like?
Most teams start by choosing one high-impact workflow, defining a small golden eval set, and building an agent with prompt-to-agent and the visual builder. Then they add retrieval, tools, and human-in-the-loop approvals, wire CI and environments, and promote via eval gates. By week four, you are usually running at least one production agent, have dashboards for stakeholders, and a clear pattern to repeat for the next use case.
10) What if we start on another stack first – can we still move to Vellum later?
Yes. If you start on LangChain, AutoGen, or a cloud SDK, you can reuse a lot of your logic and prompts while shifting orchestration and governance into Vellum. The SDKs, custom nodes, and AI Apps model make it straightforward to wrap existing logic and then gain evals, observability, and promotion workflows on top.
11) What are good alternatives if we decide not to use Vellum yet?
If you only need lightweight automations, Zapier or Make are fine. For developer-heavy, code-first experimentation, LangChain, AutoGen, and LlamaIndex are strong choices. If you are fully committed to a single cloud, Vertex AI, Azure Copilot Studio, or Bedrock AgentCore may fit. But if your priority is an AI agent builder that combines prompt-based building, AI Apps, evals, observability, and enterprise governance in one place, Vellum is still the most complete option.
This guide breaks down the top 13 AI agent builder platforms of 2026, how to evaluate them, and where each fits in enterprise adoption. We compared and evaluated these solutions to make it easy to find and evaluate the perfect AI agent builder platform for your enterprise.
Top 6 agent builder shortlist
If you only want the contenders that matter most for enterprise teams in 2026, here’s the shortlist:
Vellum: Best overall for enterprises with prompt based agent building, collaboration, evals, versioning, and observability built-in.
Vertex AI Agent Builder (Google Cloud): Best for GCP shops needing RAG, memory, and compliance.
LangChain: Best for developer-led teams that want maximum flexibility and ecosystem depth.
AutoGen: Best for multi-agent collaboration and autonomous workflows.
CrewAI: Best for “team of agents” setups with role specialization.
Dify: Best for quick, low-code prototyping and simple enterprise workflows.
I’ve only seen enterprises find org wide success with AI agents when enabled to maintain control and flexibility through the platform to keep agents adaptive to their solution. The right platform will exponentially cut the time to develop, build, and iterate AI agents for internal and external use, allowing AI initiatives to produce real value and ROI quickly.
With MIT finding that 95% of genAI pilots fail to reach production, the path forward in 2026 is choosing a platform that will be your strategic partner in AI agent building [1]. A deeply customizable platform that supports users by enabling easy building and collaborative environments is an ideal solution for this approach.
If you are looking to bring your enterprise into the modern AI world, choosing your AI agent builder solution is a pivotal step to either enable success, or if chosen poorly, become another failed initiative. We put this guide together to help you make sure the ladder doesn’t happen.
What Is an AI agent builder?
An AI agent builder is a platform or framework for designing, deploying, and managing AI agents. They are systems powered by LLMs that can reason, use tools, and act across workflows.
Here are top three platform functions to keep in mind as you evaluate AI agent builders:
Low-code & collaboration features: Makes it easy for non-technical teammates to sketch, test, and adjust workflows without needing to write full code.
Deep Developer functionality: Gives engineers the ability to extend, customize, and harden workflows with SDKs, custom nodes, and integrations.
Governance: Provides version control, permissions, audit logs, and monitoring so organizations can trust and scale their workflows safely.
Why use an AI agent builder?
Capgemini Research found that AI agents have the potential to generate $450 billion in economic value by 2028, yet 2025 showed only 2% of organizations have deployed AI agents at scale with only 12% at partial scale [2].
Enterprises leaving this massive potential on the table struggling to easily and reliably build AI agent that get into production without it breaking, drifting, or losing stakeholder trust. That’s where agent builders come in as the solution to securely build and put AI agents into reliable use.
They provide the scaffolding needed to turn promising pilots into reliable systems:
Speed up delivery: Templates, visual editors, and pre-built connectors let teams move from idea to working agent in days, not months.
Reduce risk: Built-in evaluations, monitoring, and version control mean you catch regressions before users do.
Enable collaboration: Non-technical teammates can shape workflows while engineers extend and harden them, all in the same environment.
Scale with confidence: Governance features like RBAC, audit logs, and environment separation make it possible to expand usage safely across departments.
{{ebook-cta}}
What makes an ideal AI agent builder?
The ideal platform will be unique to your business but should balance speed, reliability, and governance in a way that works for enterprise teams.
In practice, your ideal builder is one that lets your non-technical teams move fast without creating messes engineers later have to clean up, while giving engineering the depth they need to harden, monitor, and scale AI agents.
Based on what we’ve seen across the market, here’s what sets a true leader apart:
Bi-directional syncing: Visual interfaces for PMs and SMEs, plus SDKs/APIs for engineers.
Evaluation and versioning capabilities: Every release can be tested, compared, and rolled back safely.
End-to-end observability: Traces, dashboards, and logs that show how agents behave in production.
Governance at scale: RBAC, audit trails, and environment separation that meet enterprise compliance standards.
AI-native primitives: Retrieval, semantic routing, memory, and deep orchestration customizability.
Flexible deployment: Options for cloud, VPC, or on-prem, so sensitive data never leaves your control.
Healthy ecosystem: Connectors, integrations, and a vendor roadmap that signal long-term stability.
How to evaluate AI agent builder platforms?
Forget mindlessly clicking from agent builder site to site and comparing spec sheets. Here’s an evaluation framework that will ensure you make a sound, long-term choice tailored to your use case:
AI Agent Builder Evaluation Framework
Use this checklist to score each platform 1–5 and capture notes. It resizes to any screen and scrolls horizontally on small devices.
Score vendors on each dimension. 1 = weak fit, 5 = strong fit.
Evaluation Topics
Key Questions to Ask
Why It Matters
Score (1–5)
Notes
Total cost of ownership
What costs appear at scale (context, memory, tool calls)? Any limits on runs, users, or connectors?
Avoids tools that start cheap but get expensive as usage grows.
Time to value
How fast can a non-technical user ship a useful agent? How long to stable production?
Shortens pilot cycles and accelerates ROI.
Fit for your builders
Can PMs/SMEs build visually? Do engineers get SDKs, scripting, custom nodes, CI hooks?
Matches the platform to your actual team skills and workflow.
AI-native capabilities
Are retrieval, memory, semantic routing, tool use, and multi-agent orchestration first-class?
Determines whether it can power real agent use cases without brittle glue code.
Testing & versioning
Can you run evals, compare versions, promote safely, and roll back cleanly?
Prevents regressions and supports evidence-based releases.
Observability
Do you get traces, logs, and performance metrics at node, agent, and workflow levels?
Makes incidents diagnosable and improvements measurable.
Cloud/VPC/on-prem options? Private networking? Regional data residency?
Aligns with IT policies and data privacy constraints.
Performance & scalability
Latency benchmarks, throughput, concurrency limits, caching, cost controls at scale?
Ensures agents remain fast and affordable as adoption grows.
Change management
Reviews, approvals, release gates, and safe promotion across environments?
Prevents shadow workflows and keeps teams aligned.
Support & community
SLAs, live support, solution architects, active user/OSS community?
Determines how quickly you unblock issues and adopt best practices.
The top 13 AI agent builder platforms in 2026
1. Vellum AI
Quick Overview
Vellum AI is the enterprise AI-first agent builder that lets teams create production-ready agents and AI Apps using natural language prompts. Its prompt-to-agent workflow turns plain-English instructions into complete agents instantly, the visual builder makes refining logic easy for non-technical teams, and the Python/TypeScript SDK gives engineers full extensibility. With built-in evaluations, versioning, and observability, enterprises get both speed and rigor. Once agents are built, Vellum packages them into governed AI Apps so employees across the org can safely automate work without touching code.
Best For
Enterprises that want to safely enable employees to automate work with AI while giving engineering and compliance the governance, reliability, and deployment controls they require.
Pros
Fast and easy work automation with prompt based agent building
AI Apps that empower employees to run and reuse automations safely
Visual builder plus TypeScript/Python SDK for deep customization
Built-in evaluations, regression tests, and versioning
Full observability: traces, dashboards, performance metrics
Vellum is an enterprise grade AI workflow and agent builder platform that lets non-technical teammates and engineers co-build reliable, testable, observable AI agent that scale. If you care about moving from pilots to production without slowing collaboration, Vellum is the right choice.
AI Apps for safe, reusable deployment: Package agents into governed apps so employees across the org can run and reuse automations without touching code.
Built-in evaluations and versioning: Define eval sets, easily compare model and prompt variants, promote only what passes, and roll back safely.
End-to-end observability: Trace every run at the node and workflow level, track performance over time, and spot regressions before they hit users.
Collaborative agent building: Shared canvas with comments, role-based reviews and approvals, change history, and human-in-the-loop steps so PMs, SMEs, and engineers can co-build safely.
Developer depth when you need it: TypeScript/Python SDK, custom nodes, exportable code, and CI hooks to fit your existing tooling.
Governance ready: RBAC, environments, audit logs, and secrets management to satisfy security and compliance.
Flexible deployment: Run in cloud, VPC, or on-prem so data stays where it should.
When Vellum is the best fit
When enterprises want to enable all their employees to easily build agents that automate their work
Your team includes both technical and non-technical people who need to build and manage AI agents together without breaking reliability.
You plan to build AI agents that use retrieval, run across multiple steps, and need to be tracked and improved as they scale.
You want changes to be backed by testing and monitoring, so every release is based on data instead of guesswork.
How Vellum compares (at a glance)
vs Zapier / Make / Pabbly: Great for lightweight SaaS automations. Vellum is built for more comprehensive AI agent orchestration with evals, versioning, and observability at scale.
vs n8n / Pipedream: Solid for technical DIY and open-source flexibility. Vellum adds governance, collaboration, and monitoring so both engineers and non-technical teams can build together safely.
vs LangChain / AutoGen / CrewAI: Strong frameworks for developer-led customization and multi-agent research. Vellum matches their flexibility but layers in enterprise rigor—evals, tracing, and rollback—so agents don’t stall in pilot.
vs Vertex AI / Azure Copilot / AWS Bedrock: Ideal for cloud-locked enterprises. Vellum is cloud-agnostic, integrating with all major providers while adding observability, governance, and model flexibility.
vs Workato / Power Automate: Enterprise iPaaS leaders for app-to-app automation. Vellum is purpose-built for AI workflows and agents, enabling faster iteration on prompts, retrieval, and orchestration while still meeting enterprise controls.
vs StackAI / Tray.ai Good for compliance-heavy or niche verticals (safety, packaged AI, voice). Vellum provides broader orchestration plus built-in testing and monitoring for general enterprise adoption.
vs Flowise / Dify / LlamaIndex: Useful for quick prototyping or RAG-heavy tasks. Vellum delivers similar accessibility but with enterprise-grade versioning, evals, and deployment options.
What you can ship in the first 30 days
Week 1: Set up your first AI agent using templates; connect knowledge sources; define a small golden set for evals.
Week 2: Add semantic routing and tool use; wire human-in-the-loop approvals for sensitive actions; start tracing runs.
Week 3: Set up regression tests, CI integration, and multi-environment promotion; add dashboards for stakeholders.
Week 4: Expand coverage to a second use case (e.g., support macros → sales research), reuse components, and monitor cumulative impact.
Proof you can show stakeholders
Before/after evals: Demonstrate factuality and latency improvements with side-by-side runs.
Trace-driven reviews: Walk leaders through exactly what the workflow did and why.
Promotion history: Show that changes were tested and approved—not pushed blind.
Operational metrics: Volume handled, error rates, and time-to-resolution trends.
Ready to build AI agents at enterprise scale on Vellum?
Start free today and see how Vellum’s scalable infrastructure, built-in evaluations, and collaboration tools help you turn AI agents into production-grade systems org wide.
An AI agent builder lets you design, deploy, and monitor agents powered by LLMs without hand-coding all the orchestration yourself. You chain together reasoning, retrieval, tool use, and approvals in one place, usually with a visual or low-code interface plus an SDK. Strong platforms, like Vellum, also add evaluations, versioning, and observability so every change can be tested and safely promoted.
2) Why do enterprises need an AI agent builder in 2026?
Most enterprises are stuck in pilot hell because custom scripts and ad hoc frameworks are hard to govern, monitor, and scale. An AI agent builder gives you a shared, governed environment where PMs, engineers, and compliance can collaborate. Vellum does this by combining prompt-based agent building, AI Apps, evals, and observability so you can move from pilots to production without losing control.
3) Who inside the enterprise benefits most from these platforms?
Product, operations, support, and sales teams benefit because they can describe workflows in plain language and get working agents, instead of waiting on engineering queues. Engineering, data, and security teams benefit because they get SDKs, governance, and deployment controls instead of debugging one-off scripts. Vellum is designed to serve both sides: employees build and run AI Apps safely while engineering and compliance set the guardrails.
4) How do I choose the right AI agent builder for my org?
Start from your realities: cloud alignment, regulatory requirements, team skills, and the workflows you care about. If you need a balance of low-code collaboration, deep SDKs, built-in evals, and enterprise governance across clouds, Vellum is usually the strongest default. Tools like LangChain, AutoGen, or Vertex AI are better fits when you need pure code-first flexibility on a single stack and are willing to own more of the plumbing.
5) How quickly can teams see value with Vellum compared to other options?
With OSS or raw SDKs, teams often spend weeks just wiring logging, evals, and environments before real users see value. In Vellum, you can use prompt-to-agent building and templates to get a first agent live in days, then harden it with evals and observability as you go. That makes it much easier to show stakeholders real, measurable impact in the first 30 to 60 days.
6) How is Vellum different from open source frameworks like LangChain or AutoGen?
LangChain and AutoGen excel for developer-led experimentation and custom orchestration, but you own governance, monitoring, and release discipline yourself. Vellum gives you comparable flexibility via TypeScript/Python SDKs and custom nodes, but layers in enterprise features like RBAC, audit logs, eval suites, and environment-based promotion. This means your agents are easier to take from proof of concept to audited, production-ready systems.
7) How does Vellum compare to cloud-native builders like Vertex AI, Azure Copilot Studio, or AWS Bedrock AgentCore?
Cloud-native builders are strong if you want to stay deeply inside one ecosystem and you are comfortable with that vendor’s models and governance. Vellum is cloud-agnostic and multi-model, so you can mix providers while still getting unified evals, observability, and governance. Many enterprises use Vertex, Azure, or Bedrock for core infra, then layer Vellum on top as the agent-building and collaboration layer.
8) How is Vellum different from iPaaS and automation tools like Workato, Zapier, or Make?
Zapier and Make are ideal for simple SaaS automations, and Workato is great for broad enterprise integration. They are not built primarily for AI-native orchestration, evals, or prompt and model lifecycle management. Vellum focuses on AI agents: prompt-to-agent building, retrieval, routing, evaluations, and observability, while still integrating cleanly with your existing iPaaS or automation stack.
9) What does a good first 30 days on Vellum look like?
Most teams start by choosing one high-impact workflow, defining a small golden eval set, and building an agent with prompt-to-agent and the visual builder. Then they add retrieval, tools, and human-in-the-loop approvals, wire CI and environments, and promote via eval gates. By week four, you are usually running at least one production agent, have dashboards for stakeholders, and a clear pattern to repeat for the next use case.
10) What if we start on another stack first – can we still move to Vellum later?
Yes. If you start on LangChain, AutoGen, or a cloud SDK, you can reuse a lot of your logic and prompts while shifting orchestration and governance into Vellum. The SDKs, custom nodes, and AI Apps model make it straightforward to wrap existing logic and then gain evals, observability, and promotion workflows on top.
11) What are good alternatives if we decide not to use Vellum yet?
If you only need lightweight automations, Zapier or Make are fine. For developer-heavy, code-first experimentation, LangChain, AutoGen, and LlamaIndex are strong choices. If you are fully committed to a single cloud, Vertex AI, Azure Copilot Studio, or Bedrock AgentCore may fit. But if your priority is an AI agent builder that combines prompt-based building, AI Apps, evals, observability, and enterprise governance in one place, Vellum is still the most complete option.
Nick is Vellum’s technical content lead, writing about practical ways to use both voice and text-based agents at work. He has hands-on experience automating repetitive workflows so teams can focus on higher-value work.
ABOUT THE reviewer
Anita Kirkovska
Founding Growth Lead
An AI expert with a strong ML background, specializing in GenAI and LLM education. A former Fulbright scholar, she leads Growth and Education at Vellum, helping companies build and scale AI products. She conducts LLM evaluations and writes extensively on AI best practices, empowering business leaders to drive effective AI adoption.
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