Good morning.

AI agents and automations are no longer just for tech companies with deep development resources.

A new generation of no-code and low-code platforms has democratized the ability to create intelligent AI agents and workflows, putting this power into the hands of marketers, operations teams, and business analysts.

These platforms let you visually build automation sequences that incorporate sophisticated AI capabilities while requiring minimal technical expertise.

Whether you need a simple workflow to draft responses to customer inquiries or a complex multi-agent system analyzing business data, there's now a platform tailored to your specific needs and technical comfort level.

AI agents, agentic workflows, intelligent automations—let’s review and analyze all your options.

—Sam

IN TODAY’S ISSUE 🤖

• AI Agent Workflow Building Platforms of 2025

• Relay.app: Ease-of-Use for AI-Powered Automation

• Gumloop: Powerful Visual Builder for Complex AI Workflows

• Lindy.ai: Personal Business Assistant with Natural Language

• Relevance.ai: Custom AI Agents with Your Data (RAG-Driven)

• Zapier (with AI): Automation King with Basic AI Integrations

• N8N: Open-Source Automation with Custom AI Extensions

• CrewAI: Multi-Agent Orchestration (AI "Teams" Working Together)

• Flowise (Open-Source): Low-Code LLM App Builder for Developers

• Stack AI: Enterprise-Grade AI Automation with Governance

• Agent.ai: Community-Powered AI Agent Marketplace

• Choosing the Right Platform

Let’s dive in.

Platforms for Building AI Agents and Workflows

In recent years, a new class of no-code/low-code platforms has emerged to help non-developers create AI-driven agents and complex workflows. 

These tools let you visually build automation sequences that incorporate powerful AI capabilities – from having an AI research information and generate content to autonomously executing business tasks across your apps. 

Many are geared toward marketing, sales, and business operations use cases (think SaaS companies, eCommerce, and agencies), offering drag-and-drop interfaces and built-in LLM integrations (GPT-4, Claude, etc.) so you need little to no coding. 

They also typically support retrieval-augmented generation (RAG), allowing your AI agents to pull in company-specific knowledge – and connect with a broad range of business apps (e.g. Slack, Notion, CRMs, Google Drive, Meta Ads, ActiveCampaign).

Below, I’m walking you through the top platforms in this space, discussing each platform’s key features, strengths, and trade-offs. 

Also, these are in no particular order. The “best” one depends on your use case and situation. No one can tell you that. You have to try a few and figure it out.

Platforms for Building AI Agents and Workflows

In recent years, a new class of no-code/low-code platforms has emerged to help non-developers create AI-driven agents and complex workflows.

These tools let you visually build automation sequences that incorporate powerful AI capabilities – from having an AI research information and generate content to autonomously executing business tasks across your apps.

Many are geared toward marketing, sales, and business operations use cases, offering drag-and-drop interfaces and built-in LLM integrations while supporting retrieval-augmented generation (RAG).

1. Relay.app: Ease-of-Use for AI-Powered Automation

Overview: Relay.app is a modern no-code platform for building AI-powered workflows and agents. It emphasizes an intuitive visual workflow builder that lets non-technical users create automations in minutes.

You can chain together actions (triggers, data routing, etc.) and insert built-in AI steps for tasks like summarization, text extraction, or content generation. Relay also includes human-in-the-loop controls to keep you in charge of critical outputs.

AI Capabilities: Relay comes with LLM integration out-of-the-box – you can use models like OpenAI's GPT-4 or Anthropic's Claude directly without needing your own API keys. It provides a library of pre-built AI actions (for example, "Summarize text" or "Translate content") and also allows custom prompts.

While it may not support sophisticated multi-agent reasoning, it excels at injecting on-demand AI processing within standard workflows.

Integrations: The platform supports 100+ app integrations including popular business tools (e.g., Gmail, Slack, Notion, Google Sheets). This library is growing, though it may not yet match Zapier's in sheer number.

For most common marketing/sales apps, chances are Relay either has a connector or you can link via webhooks/API. This lets your AI agents not only generate content but also directly interact with external systems.

Strengths: Exceptionally user-friendly interface that makes building workflows accessible to non-techies. It includes generous AI credits and ready-made templates, so you can immediately try GPT-4/Claude in your automations.

Unlike many automation tools, Relay was designed with human checkpoints in mind. It offers affordable pricing with a free tier (up to 200 steps and 500 AI credits/month) and a Pro plan at only $9/month.

Trade-offs: Because it's newer, Relay's library of integrations isn't as extensive as incumbents like Zapier (7k+ integrations). Most popular apps are covered, but some niche services might be missing.

Relay supports conditional branches and iterations, which cover most needs, but extremely complex logic or multi-agent scenarios might be beyond its sweet spot as it focuses on simplicity.

Ideal For: Small businesses and marketers who want to automate routine workflows with a touch of AI.

For example, you can have an agent watch for new support tickets and automatically summarize each one with GPT-4 then post it to Slack, or generate draft responses to leads coming in via a form.

It shines when you need to connect a few services (say, Gmail → Notion → Slack) and incorporate AI text generation or analysis along the way.

2. Gumloop: Powerful Visual Builder for Complex AI Workflows

Overview: Gumloop is a no-code AI automation framework aimed at more advanced workflows. Think of it as a "power-user" automation tool – you drag, drop, and connect modular components ("nodes") on a canvas to design sophisticated processes.

Under the hood, Gumloop provides ready-made nodes for AI tasks like document data extraction, text analysis, scoring, etc., which you can chain together in multi-step sequences. This enables building very custom logic without coding.

AI Capabilities: Gumloop integrates various AI models and providers. It supports using OpenAI, Anthropic, and other models within different nodes, so you can even switch models per step (e.g., use a cheaper model for data cleaning and a more powerful model for content generation).

It doesn't explicitly call itself a multi-agent system, but you can achieve multi-step reasoning by linking nodes such that the output of one AI step informs the next. RAG is supported via specialized components – you can plug in a vector database for retrieval or connect to knowledge sources in a workflow.

Integrations: Aside from AI functions, Gumloop excels at pulling in documents (PDFs, spreadsheets) for processing, scraping web pages for data, connecting to analytics tools like Google Analytics and Semrush for SEO use cases, or updating records in CRM systems like Salesforce/HubSpot.

It also handles sending emails, populating forms, and other common actions. These integration capabilities mean you can use Gumloop to automate a whole business process end-to-end.

Strengths: Gumloop's node-based interface supports variables, branching, loops, and subflows, giving you programming-like control to handle complex decision trees or iterative operations. The platform comes with a rich set of pre-built "skills" for AI and data tasks, including nodes for extracting entities in text, summarizing documents, classifying data, and interacting with databases.

It natively connects to many business tools, supporting document processing, web scraping, SEO tasks, and email marketing workflows out-of-the-box. While no-code, Gumloop is designed for "power users" who demand flexibility.

Trade-offs: Because of its breadth of features, new users may find Gumloop complex at first. There's a lot to learn (understanding each node type, configuring AI prompts, etc.), so it's not as plug-and-play as simpler tools.

Gumloop positions itself for business use and is relatively pricey compared to entry-level tools, with plans starting around $97/month for basic use.

The flip side of Gumloop's flexibility is that the interface can appear busy or overwhelming to those unfamiliar with flowchart-style design.

Ideal For: Power users and teams that need complex AI-driven workflows without writing code. Examples include marketing operations specialists automating a multi-step campaign: e.g., pulling data from Google Analytics, using AI to identify insights or draft content, then automatically feeding results into a report or email.

It's also great for document-heavy processes like a legal team extracting info from contracts, or an eCommerce ops team auto-processing supplier invoices.

3. Lindy.ai: Personal Business Assistant with Natural Language

Overview: Lindy.ai takes a different approach: it bills itself as an AI assistant platform that can handle everyday business tasks via simple natural language instructions. Rather than constructing flows node-by-node, you can tell Lindy what you need or configure an agent using plain English.

For example, you could instruct Lindy to "monitor my inbox and draft polite follow-up emails to new client inquiries" and it will create an agent to do so. Its focus is on automating the kind of routine tasks an executive assistant might handle.

AI Capabilities: Under the hood, Lindy uses large language models (it's an "AI-first" product). It's somewhat model-agnostic and may allow switching between providers like GPT-4 or others as needed, though these details are abstracted away from the user.

Each Lindy "agent" is essentially powered by an LLM that has been guided with your instructions. Lindy also includes a basic knowledge base feature – you can provide context or reference info (which could be considered a form of RAG support) for the AI to use when performing tasks.

Integrations: Lindy boasts hundreds of app integrations with common tools like Gmail, Google Calendar, Slack, Microsoft Teams, HubSpot, Notion, and more. This allows its AI assistants to perform actions across your apps – e.g., sending emails, creating calendar events, posting messages, updating CRM records.

The user experience is that you connect your accounts (OAuth for Gmail, etc.) and then simply instruct Lindy on what to do, without worrying about the API details.

Strengths: The biggest selling point is that you can configure and interact with Lindy agents using everyday language. This dramatically lowers the barrier to automation.

Non-technical users don't have to think in terms of triggers or nodes; they just tell Lindy what outcome they want. Lindy excels at offloading repetitive daily chores like inbox triage, scheduling meetings based on preferences, logging information from emails into a spreadsheet or CRM, and following up on tasks.

With built-in connections to commonly used services, Lindy can seamlessly coordinate between those apps on your behalf.

Trade-offs: Since Lindy agents operate with a lot of autonomy and interpret instructions freely, there's a risk of unpredictable output. For important tasks, you have to review what the AI is doing – e.g., double-check an email draft before it sends.

Compared to a node-based builder, Lindy offers fewer controls for complex logic. You can't easily set up multi-branch conditional flows or multi-step decision trees yourself – the AI handles the flow. To customize an agent's behavior, you often have to refine the instructions/prompts you give it, which can involve trial and error.

Ideal For: Individuals or small teams looking for a virtual AI assistant to handle daily tasks without setting up complex automations.

Busy professionals like sales managers, consultants, or startup founders can use it to manage their inbox (drafting and sending common replies), schedule meetings, keep CRM data updated, or even generate quick reports from data – all by delegating to an AI agent. It's also useful in customer support or HR for triaging inquiries and drafting responses.

4. Relevance AI: Custom AI Agents with Your Data (RAG-Driven)

Overview: Relevance is a no-code platform tailored for building AI agents that are deeply integrated with your own data and knowledge. It's sometimes described as enabling an "AI workforce" for your business – essentially, you can design custom agents (even teams of agents) and equip them with knowledge from your company's data so they can operate with context.

A hallmark of Relevance is its strong support for vector databases and embeddings, meaning it's built with retrieval-augmented generation (RAG) in mind to give agents a form of memory.

AI Capabilities: Relevance supports integrating multiple AI models and crucially allows connecting those models to a vector search engine for your data. This means you can feed your proprietary data (text documents, knowledge base articles, past records, etc.) into Relevance's system. It will embed that data and let your agent query it as needed.

The agents you create can therefore "learn" from your data and use it to inform their outputs. Relevance also supports setting up multiple agents working together if needed, though it's primarily known for single-agent use with good memory. The platform includes tools for monitoring and analyzing your agents' performance.

Integrations: Relevance is somewhat more specialized, so while it can connect to external apps, its pre-built integration options are still growing. It may not have as many one-click integrations for SaaS apps as something like Zapier.

However, it focuses on data integration – allowing you to import your data from databases, knowledge bases, or via API. If an app is not directly integrated, you can often use Relevance's API or webhooks to hook into it. Many users leverage Relevance in combination with other automation tools to handle triggers and end-actions.

Strengths: Relevance's biggest strength is making RAG accessible by providing a straightforward way to give your agents a knowledge base or memory by plugging in your data.

This dramatically improves the usefulness of AI in business settings – the agent isn't just a generic GPT, it's your GPT that knows your products, policies, and metrics.

You can design bespoke agents and even orchestrate teams of agents, defining what each should do, what data they have access to, and how they interact. The platform includes monitoring dashboards to track agent performance, and it's aimed at operations teams and analysts who know their data and processes but aren't programmers.

Trade-offs: A common critique is that Relevance can become expensive for heavy usage. Free quotas exist, but once you deploy multiple agents or large volumes of data and queries, you may need higher-tier plans (which run into the hundreds per month).

While easier than coding from scratch, users have noted that more in-depth tutorials and documentation would help with concepts like embeddings or setting up data sources.

Compared to general automation tools, Relevance might not have direct integrations for every app you want. If the agent needs to take action in an external system that's not supported, you may need a developer to connect an API.

Ideal For: Relevance is a top choice when you need AI agents that leverage proprietary or large datasets.

For example, customer support is a sweet spot: companies use Relevance to build AI agents that can understand a customer's question and respond with the exact info from the knowledge base or past tickets.

Sales and marketing teams might use it to create chatbots that know the details of products and pricing, or to automate analysis of customer feedback. Operations and HR could use it to build an internal Q&A assistant that employees query for company policies or data.

5. Zapier (with AI): Automation King with Basic AI Integrations

Overview: Zapier is one of the original no-code automation platforms, widely used to connect apps and move data around with simple triggers and actions. While not an AI agent platform per se, it recently added AI features to keep up with the trend.

With Zapier, you can set up workflows (called "Zaps") such that "When X happens in App A, do Y in App B," and now include an AI step in those Zaps, typically using OpenAI's models. Zapier also launched Natural Language Actions (NLA) which allow LLMs to directly interact with the 7,000+ apps Zapier supports.

AI Capabilities: Zapier's native AI capability is relatively straightforward: it has a built-in OpenAI integration where you can specify a prompt to send to GPT-3 or GPT-4 and use the result in subsequent steps. This covers things like text generation, transformation, or classification within a workflow.

However, Zapier does not offer advanced agentic behavior out-of-the-box – there's no concept of the AI planning multiple steps on its own or retrieving knowledge dynamically. Essentially, Zapier can invoke an LLM for a single-step task (like "generate text based on input").

Integrations: This is where Zapier shines – it has over 7,000 app integrations available. Virtually any popular SaaS or service you can think of (and many niche ones) are on Zapier: from Facebook Lead Ads to ActiveCampaign, from Google Sheets to Salesforce, Slack, Notion, Shopify – you name it.

This enormous ecosystem is Zapier's core strength. For marketing and sales teams, this means if you need to connect to an ad platform, CRM, email marketing tool, analytics platform, etc., Zapier likely has pre-built connectors.

Strengths: With thousands of supported apps, Zapier can connect your AI agent to almost any tool your business uses. This is critical because an AI agent is only as useful as the actions it can perform.

Zapier has been around since 2011, so it's a very stable, well-documented platform with ample community support and tutorials. There are thousands of pre-built "Zap" templates shared by the community, including many that incorporate the OpenAI integration.

If your goal is to chain together multiple services and have an AI do one part of it, Zapier is very efficient.

Trade-offs: Zapier's AI integration is not as advanced as dedicated AI agent platforms. It doesn't natively support things like long-term memory, tool use by the AI, iterative reasoning loops, or multi-turn conversations.

Each AI step is stateless and the workflow logic is fully predetermined by you. Zapier's pricing is based on number of tasks (actions) run, and introducing AI can increase task count and costs.

For example, a single email-to-summary-to-Slack workflow might use 3 tasks every time it runs. If you're doing this hundreds or thousands of times, you may quickly need a higher Zapier plan.

Ideal For: Zapier remains a top choice whenever broad app integration is the priority. If you have an existing automation workflow and just want to sprinkle in some AI (like generating text or categorizing data), Zapier lets you do that quickly.

It's ideal for marketing teams automating multi-channel activities, e.g., whenever a new product is added (in Shopify or a DB), use AI to create a description, then post it on multiple platforms. Or for sales ops – e.g., when a lead comes in, use AI to enrich the lead or draft a personalized intro email, then log it all in the CRM.

6. N8N: Open-Source Automation with Custom AI Extensions

Overview: n8n is an open-source alternative to Zapier – a workflow automation tool that you can self-host and extend. It provides a visual node-based editor to connect different services and build automations very similar in concept to Zapier or Make.com.

The big difference is n8n is open-source and can be run on your own server for free (or you can pay for a cloud-hosted version).

Out of the box, n8n has a wide range of integrations and the ability to call any API. It doesn't have built-in AI modules by default, but because you can use API calls or install community-contributed nodes, you can incorporate AI fairly easily.

AI Capabilities: Since n8n doesn't come with dedicated AI features, you leverage AI by calling external APIs from your workflows. For instance, you can use an HTTP Request node to call the OpenAI API (GPT-4, etc.) with a prompt, then use the response in subsequent nodes. The community has created custom nodes for OpenAI, Hugging Face, and other AI services to make this easier.

This means you can build AI agents with n8n – for example, an n8n workflow could: receive an input, send it to an LLM for analysis, then based on the AI's answer take one of several routes (emulating an agent decision).

Integrations: n8n supports a broad set of integrations out of the box and can connect to any REST API with some configuration. It has nodes for many popular apps (Google services, Slack, CRM systems, databases, etc.), albeit fewer than Zapier's thousands. Importantly, because it's extensible, you or the community can add new integrations by creating nodes.

You also have low-level control: with an HTTP node and some JSON, you can integrate with virtually anything online. If you have in-house databases or internal APIs, n8n is great because you can connect to those securely on your own infrastructure.

Strengths: n8n's source code is open (fair-code licensed) and you can run it on your own server for free. This is a huge plus for those concerned about data privacy (you keep all data in-house) or cost control at scale (no per-task fees – your only cost is the server it runs on). Because you have access to code, you can write custom nodes or logic to do anything not already supported.

There's an active community of n8n users and contributors sharing how to use n8n for various tasks, including AI integrations. If you use n8n self-hosted, you aren't paying per task or API call (beyond the AI API itself), which can save money for businesses automating a lot of tasks.

Trade-offs: Let's be clear – n8n is more developer-friendly than true no-code for novices. Non-technical users may struggle with things like configuring authentication for APIs, understanding JSON data structures that flow through nodes, or setting up the server.

If you self-host, you are responsible for installing n8n (usually via Docker or similar), managing updates, scaling the server if needed, and ensuring uptime.

The n8n editor works well, but some find it less slick or intuitive compared to commercial competitors. The documentation is good but not as extensive as Zapier's, for instance.

Ideal For: n8n is perfect for organizations that need maximum flexibility and control. If you have a tech team or at least a savvy developer or sysadmin, n8n can be a backbone for extensive automations, including AI-driven ones. It's used by startups to build internal tools (for example, orchestrating various AI analysis on data, then writing results to a database, all within an n8n workflow).

It's also popular for those who are privacy-conscious – e.g., a healthcare or finance company might prefer n8n so that all data stays on their own servers while the AI processing happens via secure API calls they control.

7. CrewAI: Multi-Agent Orchestration (AI "Teams" Working Together)

Overview: CrewAI is a cutting-edge platform designed for scenarios where multiple AI agents collaborate to perform a task. Inspired by research on "AI swarms" or team-of-agents approaches, CrewAI lets you set up a group (or "crew") of AI agents, each assigned a specific role, and define how they coordinate in a workflow.

For example, imagine automating an incident response: one agent gathers log data, another agent analyzes it for anomalies, a third agent writes a summary report. CrewAI would enable all three to work in concert within one automated pipeline. Uniquely, it offers both an open-source Python framework and a no-code Studio UI.

AI Capabilities: The core capability here is multi-agent support. Most other platforms run one agent at a time (maybe sequentially). CrewAI is built from the ground up so that you can have multiple agents running simultaneously or in a coordinated sequence within a single workflow. E

ach agent can have its own LLM (CrewAI is model-agnostic, supporting OpenAI, Anthropic, or even local models) and its own tools/permissions.

You define how they communicate – e.g., agent A's output could become agent B's input, or they might all share a common memory store. This approach can solve complex tasks by breaking them into specialized subtasks handled by different AIs.

Integrations: Currently, CrewAI's focus is on the agents themselves, with relatively limited third-party integrations out-of-the-box. It's more akin to an AI orchestration engine that you might integrate with other systems, rather than a full-fledged app integration hub.

That said, you can certainly have agents in CrewAI that use tools/APIs to interact with external apps – for example, one agent could have access to a Slack API key and be tasked with posting a message as its action. As the product matures, we might see more native integration modules.

Strengths: CrewAI is unique in enabling multiple AI agents to work simultaneously on a task. This can significantly expand what's possible – for example, one agent could be planning a strategy while another executes steps of that plan.

Despite the complexity of coordinating agents, CrewAI's Studio interface lets you drag-and-drop to define roles and flows. Because it's built with parallelism in mind, CrewAI can scale to enterprise workloads, running many agents in parallel or handling large jobs efficiently.

The availability of an open-source framework is a boon for developers who want to deeply customize or extend multi-agent logic.

Trade-offs: The concept of designing multiple agents is fairly new and can be hard to grasp for users who are just getting used to single-agent automation.

There's likely an educational gap – you have to figure out how to break your problem into roles, decide how agents communicate, etc. CrewAI doesn't yet match the breadth of integrations that more established automation tools have.

If your multi-agent workflow needs to interact with many external systems, you might need to put in some custom work. Being on the frontier, CrewAI's community, examples, and support resources might be smaller.

Ideal For: CrewAI is best for organizations or innovators who truly need multiple AI agents collaborating – for instance, complex workflows that benefit from task specialization or parallel processing.

Some examples: a content production pipeline where one agent generates a draft, a second agent fact-checks or improves it, and a third agent formats or publishes it.

A data analysis pipeline where different AIs handle data gathering, analysis, and reporting as separate functions. It's also great for R&D and pushing the envelope – AI consultancies or forward-thinking teams might use CrewAI to prototype the future of AI-driven operations.

8. Flowise (Open-Source): Low-Code LLM App Builder for Developers

Overview: Flowise is an open-source low-code platform for building LLM-powered applications and agents. It provides a visual drag-and-drop interface to construct flows that involve Large Language Models, much like how one would build a pipeline in LangChain but without writing code.

In fact, Flowise is often compared to LangChain with a UI on top. With Flowise, you can create chatbots, Q&A systems over documents, or more general AI agents that use external tools (like web search or calculators) in their reasoning. It's developer-friendly but also approachable enough that non-programmers with some technical inclination can use it.

AI Capabilities: Flowise shines in enabling you to incorporate advanced LLM functionalities: it supports memory modules, tool usage, multi-step reasoning, and connecting to different LLMs and vector databases. For example, you can set up a flow where the LLM first retrieves relevant info from a vector store (RAG), then answers a question, then perhaps uses an API tool to fetch something it doesn't know.

It supports many integrations including LangChain and LlamaIndex and can work with 100+ other services for inputs/outputs. Notably, you can plug in OpenAI models, open-source models via API, or even local models. It's particularly strong for document question-answering tasks.

Integrations: Flowise, being open source, is very extensible and integrable. It might not have one-click integrations to every SaaS app (like posting to Slack, etc.), but because you can use API nodes or code, you can connect it to virtually anything. It's commonly used in conjunction with chat interfaces – e.g., embedding a Flowise-powered chatbot on a website. For business workflows, one might use Flowise as the "AI engine" and another tool to trigger it or handle non-AI steps. Its strength is more on the AI side (data sources, AI models, output formats) rather than pre-built connectors to business apps.

Strengths: Flowise provides a user-friendly UI to construct complex LLM flows. This lowers the barrier for prototyping sophisticated agents – you can drag nodes for the LLM, for memory, for tools, and connect them rather than writing boilerplate code.

It's excellent for quickly iterating on an idea. It's free to use and self-host, meaning you have full control.

he community can contribute new nodes or fixes, and you can modify it to fit your needs. Flowise is not tied to one AI vendor – you can use OpenAI, local models, Hugging Face, etc., and connect to vector DBs like Pinecone or others. Developers appreciate that with Flowise, you can test your agent flows in real-time in the UI and see where things might be going wrong.

Trade-offs: To get started, you need to install Node.js and set up Flowise (or use Docker). It's not extremely hard, but it's not a simple sign-up like SaaS products.

While basic chatbots are easy, to fully leverage Flowise's power (like using memory effectively, or writing custom functions), you need some understanding of LLM concepts and maybe coding.

Being a community project, Flowise's interface, while good, may not be as polished as paid platforms, and documentation could lag behind features. Flowise is fantastic for building the AI brain of your agent, but it's not an all-in-one business automation suite.

Ideal For: Flowise is a top pick for developers, data scientists, or technically oriented folks who want to build custom AI-driven apps or agents quickly. It's excellent for creating chatbots that answer based on specific documents or data – e.g., an internal bot that answers employee questions using your company's Confluence/Notion pages (with Flowise handling the retrieval and QA).

It's also great for prototyping Agent-type applications where an AI needs to use tools: like an agent that, given a query, will call a weather API or search engine and then formulate an answer. Companies might use it to build internal assistants without exposing data to third-party SaaS.

9. Stack AI: Enterprise-Grade AI Automation with Governance

Overview: Stack AI is a low-code platform aimed at enterprises that want to deploy AI agents/assistants with a strong emphasis on security, compliance, and monitoring.

It enables companies to build custom AI-driven applications (from customer-facing chatbots to internal workflow automations) without coding, similar in spirit to others, but its distinguishing factor is the set of enterprise features it offers.

Essentially, Stack AI not only lets you create AI agents, but also helps you manage and govern them at scale – so IT and management can trust what's happening. It offers features like analytics dashboards, audit logs of agent decisions, version control for your agent configurations, and role-based access control for team collaboration.

AI Capabilities: Stack AI supports building the same kinds of agentic workflows as other platforms – connecting triggers, calling LLMs, integrating data sources, and performing actions. It likely integrates mainstream LLMs and possibly allows others.

It also has a knowledge base integration feature, allowing agents to connect to company databases or knowledge for context, which means RAG is on the table for creating more intelligent agents that leverage internal data.

The platform emphasizes that you can track how agents are performing (success rates, response times, etc.), which implies they provide tools to fine-tune and ensure quality of the AI's output over time.

Integrations: Stack AI is built to fit into corporate environments, so it provides integration options for enterprise software and databases. This might include connectors for systems like SAP, Oracle, SharePoint, or popular enterprise SaaS with an eye on security.

It likely can connect with CRM systems, customer support platforms, and other business apps too. It also supports different deployment options (cloud, on-premises, hybrid), meaning it can integrate within a company's private cloud or data center, which is crucial for some industries. This flexibility allows integration not just at the app level but at the infrastructure level.

Strengths: Stack AI provides features like audit logs, permissions, version control, and sandbox testing for your AI agents.

This means if an agent sends an email or modifies a record, there's a trace of it. If something goes wrong, you can roll back to a previous agent version. These capabilities are huge for industries with compliance requirements.

It's designed to plug into enterprise IT environments. You can deploy it on-prem or in a private cloud, satisfying strict IT policies. Stack AI supports multi-user collaboration with role-based access. For example, a data scientist and a business analyst could work together on an agent, with proper permissions.

Trade-offs: All those enterprise features come at the cost of a more complex interface and setup. A newcomer might find the platform heavy, with many settings and configurations. It's not as immediately intuitive as some lightweight tools.

Stack AI's pricing is on the high end. It's targeted at mid-to-large enterprises, so even the "Starter" plan is relatively expensive (around $139/month) and higher tiers go into many hundreds or custom pricing.

If you're a small team or just need a simple marketing automation, Stack AI will feel like using a cannon to shoot a fly. Because it prioritizes reliability, Stack AI might be a bit slower in adopting the absolute latest AI bells and whistles.

Ideal For: Stack AI is ideal for large companies and enterprises that need AI automation at scale with full oversight.

Think of a bank or healthcare company that wants to deploy an AI assistant for customer support – they could use Stack AI to ensure the assistant only uses approved info, logs all interactions, and complies with data retention policies.

Or a multinational corporation could use it to build an internal AI tool for employees (like an HR help bot) knowing that they can monitor usage and keep data internal. Agencies or firms working with sensitive client data might also prefer Stack AI's controlled environment.

10. Agent.ai: Community-Powered AI Agent Marketplace

Overview: Agent.ai (not to be confused with generic "AI agents") is a unique entry that combines a no-code agent builder with a marketplace/network of pre-built AI agents.

Founded by Dharmesh Shah (co-founder/CTO of HubSpot) in late 2024, Agent quickly gained traction, amassing over 500,000 users within months of launch.

The concept is that users can create AI agents for various business tasks and share them on the network, or browse and use agents others have created.

It's been described as "the #1 professional network for AI agents", somewhat like an app store, but for AI workflows. You can one-click deploy these or clone and tweak them using the builder interface.

AI Capabilities: Agent's agents are essentially multi-step workflows that can involve LLM calls and integrations, similar in capability to what you'd build on platforms like Zapier or Relay (the difference being many are premade by the community).

The platform integrates with OpenAI's GPT models – in fact it's noted that they integrated OpenAI's tech early on. Agents can be triggered manually or by certain events, and they can chain multiple actions.

For instance, an "email triage" agent might read an incoming email with GPT, decide if it's a common question, draft a reply, and then either send it or flag for human review.

Integrations: At launch, Agent.ai's integration focus is logically around HubSpot and related marketing/sales tools, given Dharmesh's involvement and target user base.

It's said that these agents can chain actions and integrate with business apps – likely examples include connecting to your email (Gmail/Outlook) to read/send messages, CRM systems (HubSpot CRM, Salesforce maybe), social media APIs to post content, and knowledge bases like Notion or Confluence to fetch info. The platform is probably adding more connectors as users demand them.

Strengths: By far the biggest strength is the crowd-sourced library of agents. With so many users, thousands of agents have been created and many are shared.

Over 13,000 ratings were submitted on agents in the first few months, meaning users are actively trying and reviewing each other's agents. For those who do want to create custom agents, Agent.ai provides a simple low-code builder interface.

The agents on the platform skew toward marketing, sales, and support tasks. This focus means if you're in those domains, you'll find many relevant examples or templates. The fact that it hit half a million users in six months suggests a lot of momentum and rapid development.

Trade-offs: Since anyone can publish an agent, not all shared agents are great. Some might be poorly designed, not thoroughly tested, or too generic.

While the rating system helps, a new user might still have to sift through or experiment to find an agent that truly meets their needs.

The builder is low-code and optimized for simplicity, so truly complex logic might not be possible. It's unclear how many integrations are available at this stage.

The platform likely started with a focus on the agent logic, and less on connecting to every possible app. Being new and in rapid growth, there's always a risk that features change, pricing models shift, or certain agents break as the platform updates.

Ideal For: Agent.ai is fantastic for small businesses, marketers, and entrepreneurs who want quick wins with AI automation. If you're not a developer and not even that into building workflows, you can still leverage AI by simply picking agents others made.

For instance, a small e-commerce shop owner could find an agent that analyzes customer reviews and sends a summary every week, or one that writes product descriptions from specs – and use it with minimal effort.

Marketing agencies might use Agent.ai to jumpstart campaign content creation or reporting tasks by grabbing existing agents and adapting them.

Choosing the Right Platform

When selecting a platform, consider:

• Ease of Use: If you need broad accessibility and speed, something like Relay.app or Agent.ai is great for starters. If you have more technical comfort, Flowise or n8n offer more power.

• Complexity of Tasks: For simple linear workflows, any platform will do. For complex reasoning or multiple agents, you'd look at Gumloop (advanced logic) or CrewAI (multi-agent).

• Integration Needs: If you rely on a wide variety of apps, Zapier (or Make.com) is the integration king. Relay and Lindy also support many popular apps. For custom or on-prem integrations, open-source options like n8n or enterprise solutions like Stack AI shine.

• Business vs. Technical Focus: Be honest about your team's skillset. No-code platforms (Relay, Lindy, Agent.ai, etc.) are made for non-programmers. If you have developers excited to tinker, they might prefer the flexibility of Flowise or an open framework like CrewAI.

• Scale and Governance: For a few automations in a small company, a nimble SaaS tool is fine. But if you're in a large org with compliance needs, something like Stack AI or a carefully self-hosted solution might be necessary.

Each platform lowers the barrier to implementing AI-driven automation in business contexts, with different strengths.

By selecting the one that aligns with your team's skills, your workflow complexity, and your integration needs, you can accelerate adoption of "agentic AI" in your marketing, sales, or operations processes.

The rise of these AI agent building platforms represents a significant shift in how businesses can leverage artificial intelligence.

No longer restricted to companies with technical teams, the power to create intelligent workflows is now accessible across all business types and teams.

As you evaluate which platform best fits your needs, consider not just your current requirements but how you might scale your AI automation in the future.

Start small with a focused use case, experiment with the platform that aligns with your technical comfort level, and gradually expand your automation ecosystem.

These platforms offer varying approaches to solving similar problems, but they all share one common goal—empowering you to build an AI-enhanced workflow that saves time, reduces errors, and allows your team to focus on higher-value work that truly requires human creativity and judgment.

Talk soon,
Sam Woods
The Editor