AI Agent Platforms of 2025

The Rise of AI Agent Platforms in 2025

In 2025, I’ve noticed a trend: specialised AI agent platforms are popping up to simplify the creation and deployment of intelligent agents. These platforms do not possess the power of general-purpose AI tools but offer a canvas for cobbling together multi-agent systems, automating business processes, and plugging into enterprise software without building from scratch. 

Rapid Deployment and Cost Reduction

Specialised AI agent platforms allow agents to be launched in days rather than months. This reduces development costs by up to 60–70% and accelerates Go-To-Market timelines.

Business Process Automation

These platforms enable agents to handle customer support, data analysis, and workflow orchestration simultaneously. For example, agents can qualify sales leads, update inventory, and provide daily performance summaries across Slack, Teams, and CRM systems.

Built-in LLM Integration

Integration with large language models like Claude 4.5, Sonnet, ChatGPT, and Google Gemini provides advanced natural language understanding without manual coding. This lowers technical barriers and allows non-technical teams to participate directly in agent setup.

Scalability and Security

Modern platforms include role-based access controls, encrypted data handling, and real-time monitoring dashboards. This ensures agents are secure, scalable, and easy to supervise, while workflows can be adjusted in response to user feedback.

Flexibility and Control

Enterprises can quickly prototype, iterate, and deploy efficient AI agents. These platforms provide flexibility and full control, allowing businesses to use AI as a critical day-to-day tool.

Features to Look for in an AI Agent Platform

When I’m analysing AI agent platforms in 2025, there are a few Jenga pieces that make up the platform and work together to see if it’ll be able to meet my business and development needs. It could save us weeks of groundwork and certainty that agents are functioning reliably in the “real world”.

• Integrations: I take into consideration how well the platform integrates with the tools that I love and depend on, including CRM systems (Salesforce, HubSpot), Slack or Teams, Shopify and cloud databases. For example, I integrated my agent with Salesforce to automate lead management, which led to 40% less manual follow-up.
• Support for LLMs: State-of-the-art AI agents are now based on large language models, such as Claude 4.5 Sonnet, ChatGPT, and Google Gemini. I like to work with platforms that allow me to control or mix these models so that the agents are capable of managing rich queries, retaining context and responding naturally across channels.
• Visual Editing: I work best when I have a drag-and-drop or flowchart style of editing, so that’s always a huge plus. It allows me to design multi-step processes, jump branching tasks and set triggers without us writing much code, thus making deployment a breeze and enabling easy participation from non-tech members of the team.
• Customization: All businesses work a little differently, so being able to alter logic, dictate intents, and configure conditional operations is important to me. This agility allows us to ensure that agents can embody our brand voice and effectively manage niche workflows.
• Security of Data: All customer and operational data must be protected with no exceptions. Apart from that, another important aspect for me is encryption, role-based access control and GDPR readiness, as long as sensitive data must be kept safe and agents work smoothly.

Based on experience, the platforms that have these features allow me to publish AI agents that are strong, trustworthy, and flexible. Selecting an LLM tool with integration capability, great flexibility coupled with excellent visual workflows, customisation, and security guarantees me that my agents are up to speed today, but also flexible and extensible into the future as business needs expand. It is these capabilities that make a platform a strategic asset, not just a technical tool.

Gumloop — Simplifying AI Agent Creation

When I found out about Gumloop, I was excited about its ability to make my work on AI agent creation easier and with no need for coding skills. Built for small and midsize businesses, this solution allows users to automate anything with a visual drag-and-drop editor. Key Features of Gumloop:

• Visual Workflow Builder: Users are able to visually envision and create workflows by linking together pre-made “nodes” that perform a function, such as extracting data, analysing with AI, or sending an email. This modular fashion allows for the creation of advanced agents customised to business requirements.
• Integrates with Your Workflow: Gumloop offers out-of-the-box integration with popular tools such as Slack, Zendesk or Google Sheets and Perplexity, allowing you to automate tasks across the apps without getting your hands dirty.
• Large Language Model Support (LLMs): The platform embeds leading-edge LLMs, which enable users to harness natural language processing in their agents for tasks like sentiment analysis, content generation and customer support.
• Template Manager: Gumloop provides a number of pre-built templates for popular use-cases such as Lead Qualification, Social Media Sentiment Analysis, and Customer Support Triage. These templates can be customized by end users to suit their particular needs, freeing up time and resources in the development cycle.
• Performance and Security: The platform offers enterprise-class capabilities, such as fine-grained access controls, audit logs, and virtual private cloud deployment options, enabling data security at any scale.

I find Gumloop has been a great tool to automate repetitive work and deliver operating leverage. Its approachable interface and powerful capabilities also mean it’s a good fit for companies that don’t have a ton of technical know-how but are still looking to use AI.

Relay.app — Focus on Workflow Automation

For complex workflows across multiple tools that I need to automate, I go with Relay. App. With this platform, I can build AI agents that perfectly fit with 100+ applications like HubSpot, Notion, Slack and Google Sheets. Key Features:

• Visual Workflow Builder: I am able to build new workflows with simple drag and drop triggers and actions, easily connecting them without having to actually write code.
• AI Integration: Relay. The app supports AI models like ChatGPT, Claude, and Google Gemini to help me add natural language processing abilities to my agents.
• Human-in-the-Loop: I can add manual approval steps to make sure that important decisions get checked before taking effect.
• Real-time Data: The platform continuously checks for changes on nested objects so that the data used in my automations is always the latest.

Stack AI and HockeyStack — Analytics and Data-Driven Agents

My go-to, when I have to develop AI agents that are datacenters, is Stack AI and HockeyStack. They allow me to build intelligent agents that can do stuff on receiving commands and decide using real-time analytics.

With Stack AI, I can create AI agents that are able to learn structured information from unstructured sources like PDFs and forms with the support of its embedded OCR. Also, I can perform Retrieval-Augmented Generation to retrieve the quoted answer from my encounter knowledge and make sure my agents use correct and fresh information.

HockeyStack, in contrast, natively connects to my GTM tech stack and pulls all of structured & unstructured data across marketing, sales, product, and customer success from it. This capability will enable me to build AI agents with real-time, contextually relevant knowledge, which can help power business results.

By using these platforms, I can make AI agents not just intelligent, but data-driven so that you are ensured of it providing value for multiple business areas.

Voiceflow, OpenAI Agent Builder, Devin AI — Versatile AI Agents

When I’m working on creating AI agents that are more general and have some level of customisation, I use the likes of Voiceflow, OpenAI Agent Builder, and Devin AI. There are also the unique attributes of each platform that resonate with different types of use cases and technical needs, which, being a developer’s advocate, I can produce custom solutions for web, mobile, and consumer, business applications for enterprises.

Voiceflow

• Visual Workflow Builder: You can build AI agents within task-oriented conversations via the drag-and-drop interface to create complex conversational flows with no code. This methodology results in dramatic savings of development time and allows non-technical members of the team to participate in the design of the workflow.
• Multi-Channel Support: You can deploy agents on various platforms such as Web, Mobile and Voice Interfaces, using voice flow here. This allows my agents to connect with users in different situations and helps us create a more uniform experience for our customers.
• Integration Capabilities: You may built agents that link to 3rd party APIs and services, enabling them to perform a wide array of functions from updating CRM systems through to creating relevant content on the fly. It is this ability (also from) that allows my agents to seamlessly interact with real business systems.
• Team Work: The Solution provides teamwork and different developers can work on the same project in parallel. Support for version control, commenting, and shared workspaces minimises misunderstandings and speeds up communication.

OpenAI Agent Builder

• Node-Based Design: You can create agents using nodes for tasks or actions in a modular way. This allows you to easily refactor logic and experiment with different workflow structures.
• Custom Logic: You can define the logic and behavior of your agents using OpenAI’s Agent Builder. Conditional branching and triggers let you control how the agent reacts to different scenarios.
• Code Export: You can export an agent’s source code for further tuning or integration with other systems. This makes it easy to slot agents into existing pipelines without restrictions.
• Enterprise-Grade Features: You can take advantage of enterprise-level features such as version control, fine-grained security, project organization, delegation, and policy enforcement. These tools help maintain control and consistency across multiple projects.

Devin AI

• AI Streaming Software Engineer: You can use Devin AI as an AI software engineer that writes, runs, and tests code autonomously. This feature shortens development cycles by reducing the need for manual scripting and improving precision in complex scripts.
• Code Refactorings: You can leverage Devin AI to refactor codebases, making them more maintainable and performant. It suggests optimizations and standardizes code layout to ensure long-term scalability.
• Data Engineering Support: You can get assistance with data warehouse migration, ETL development, and data cleaning. Devin AI helps prepare high-quality data for AI models and analytics.
• Development Tool Integrations: You can integrate Devin AI with tools like GitHub and CI/CD pipelines. This allows you to deploy, test, and manage version control directly on the platform without switching tools.

AirOps, Zep, Postman AI Agent Builder — Developer-Centric Platforms

When building AI agents that need to be smoothly plugged into the systems they target, I use AirOps, Zep ,and Postman AI Agent Builder. These are developer-friendly platforms that come with powerful APIs, SDKs and tools to make the integration hassle-free, as well as offering maximum flexibility and control.

AirOps

• RESTful API: AirOps provides a robust REST API that allows for programmatic use of the platform to automate or integrate with other services. I can do as for example, directly calling endpoints from scripts or make use of them as part of larger enterprise workflows.
• Integration with workflows: It allows me to actually use outside APIs, for example, through the API Utility Step, which helps in easing communication with services on my behalf. This feature enables you to have an agent with dynamic actions posting to real-time from multiple systems.
• Data Source Connections: AirOps has connection support for plenty of data sources like BigQuery, Postgres, and Snowflake, so I can use the current data to fuel my AI agents. This way, agents work on the latest information.

Zep

• Multi-Language SDKs. Zep provides Software Development Kits (SDKs) for my favourite programming language — Python, TypeScript/JavaScript and Go, allowing me to develop and run AI agents in my preferred language! This allows me to easily use agents that fit existing development domains, even in very complex environments.
• Model Context Protocol (MCP): Zep effectively supports the Model Context Protocol (MCP). which allows me to define and maintain the context in which my AI agents work, so they understand what’s going on around them before embarking on their tasks. This feature reduces errors and adds to the robustness of the device.
• Open-Source Flexibility: Being open-source, Zep allows me to adapt and extend it as I need. I can change the core codebase, create custom modules, and submit enhancements back to the community.

Postman AI Agent Builder

• Rich API Ecosystem: Postman integrates with more than 18,000 APIs that make it possible to attach a wide range of services to the AI agents. This aptitude makes it possible for agents to streamline, pull, push is and leverage data across different systems.
• Model Support: The platform supports models provided by other companies, such as OpenAI, Anthropic, Meta (so you can choose the best model for each of your requirements). Further, I can switch models or mix them and obtain hybrid functionalities.
• Tool Generation API: Postman’s Tool Generation API enables you to quickly create tools ready for agents, accelerating the development process. The automation of such tasks and the ability to script and scale deployments without human intervention are powerful features.

Cost, Scalability, and Support Considerations

When I decide on an AI agent platform in 2025, cost, scalability, and support are three things that I think of as a single bundle, because it’s about how the product will adapt to my business over time and actually handle real workloads. Here’s a quick comparison, from my experience:

Conclusion

After exploring AI agent platforms in 2025, I’ve realized that selecting the right tool depends on goals, budget, and technical expertise. For small businesses or quick deployments, ChatbotBuilder.ai or Windsurf offer affordability and simplicity. If automation and integration across multiple apps are key, I prefer Zapier Chatbots or Relay.app. 

Developers seeking deep customization and API access benefit from AirOps, Zep, or Postman AI Agent Builder. For data-driven insights, Stack AI and HockeyStack excel. Assessing scalability, support, and pricing ensures I choose a platform that delivers efficiency, flexibility, and long-term value for my projects.