What are the best tools for building and deploying AI agents?

The best tools for building and deploying AI agents fall into a few categories: **no‑code/low‑code builders**, **developer frameworks**, and **cloud/enterprise platforms**. The right “best” choice depends on whether you’re a non‑technical user, a product/ML team, or an enterprise IT org.

Below is a concise, opinionated map of the landscape, with examples in each category.

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## 1. No‑code / low‑code AI agent builders

Best if you want to ship useful agents fast (prototypes, internal tools, lead gen, support bots) without deep engineering.

**Glide** – business apps & operations agents

- **What it is:** No‑code platform for building AI‑powered business apps and agents (internal tools, workflows, dashboards).

- **Why it’s good:** You can build agents that use your data, integrate with existing systems, and deploy as web/mobile apps in days; aimed at operations and business teams rather than engineers.[1]

- **Strengths:** Very fast iteration, app-like UI, enterprise-friendly integrations and governance.[1]

**Voiceflow** – conversational assistants (chatbots, IVR, voice)

- **What it is:** Visual builder for multi‑turn chat/voice agents, widely used for customer support and assistants.[2]

- **Why it’s good:** Great for designing flows, testing with real users, and deploying to web chat, call centers, and more.[2]

**Relevance AI, Make, and similar automation‑oriented tools**

- **Relevance AI:** Lets you visually build agents and workflows (e.g., lead gen bots) and connect them to channels like WhatsApp or Instagram without code.[3]

- **Make (formerly Integromat):** Automation platform with an “agent” module that can call tools, webhooks, and external APIs to orchestrate complex flows.[3]

- **Use when:** You’re combining LLM reasoning with lots of SaaS integrations and don’t want to manage infrastructure.

**Stack AI, Gumloop, Relay.app, Cofounder, AirOps, etc.**

- These are modern no‑/low‑code agent builders and workflow tools:

- **Stack AI:** No‑code platform to build AI workflows and agents using drag‑and‑drop components.[2]

- **Gumloop / Relay.app / Cofounder / AirOps:** Focus on automating business processes, sales/marketing workflows, or internal tools with AI agents.[2]

**When these are “best”**

- Non‑developers or lean teams wanting:

- Lead‑gen, support, or ops agents

- Prototypes for client work

- Automation around SaaS tools (CRM, ticketing, email)

- You prioritize **speed and UX** over fine‑grained control of models, infra, and evaluation.

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## 2. Developer‑focused agent frameworks & libraries

Best if you’re an engineer who wants flexibility, custom tools, and complex multi‑step/multi‑agent workflows.

**LangChain** – general‑purpose agent framework

- **What it is:** A widely used open‑source framework for LLM apps and agents.

- **Why it’s good:** Provides abstractions for tools, memory, retrieval, and multi‑step reasoning, plus a huge ecosystem of integrations.[5][6]

- **Best for:** Python/JS developers building production‑grade agents that call many tools and APIs.

**CrewAI** – multi‑agent “crew” orchestration

- **What it is:** A framework for orchestrating multiple specialized agents (researcher, planner, writer, executor) that collaborate.[5][6][7]

- **Why it’s good:** Designed for complex workflows where different roles coordinate to achieve a task.[6][7]

**AutoGen** – programmable multi‑agent conversations

- **What it is:** A framework originally from Microsoft for building agents that converse with each other and tools to solve tasks.[5][7]

- **Why it’s good:** Strong for research, experimentation, and sophisticated multi‑agent setups.[5][7]

**Tooling and infra around agents (Zep, Postman, etc.)**

- **Zep:** Provides “scalable blocks” like long‑term memory and retrieval to turn prototypes into production‑ready agents.[2]

- **Postman:** Increasingly used to test and orchestrate tool‑calling and API interactions for agents.[2]

**When these are “best”**

- You need:

- Fine‑grained control over tools, prompts, memory, and evaluation

- Multi‑agent or multi‑step workflows beyond simple “chatbot” use cases

- Integration with your existing codebase, CI/CD, and observability.

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## 3. Cloud & enterprise AI platforms

Best if you’re building agents that must integrate deeply with your cloud stack, data, security, and governance.

**OpenAI Agents & tools APIs**

- **What it is:** OpenAI’s new “agents” tooling: APIs and primitives designed explicitly for agentic apps (tools, files, state, etc.).[4]

- **Why it’s good:** You get a managed agent runtime closely integrated with frontier models and tool calling.[4]

- **Best for:** Teams already using OpenAI who want to offload some of the orchestration to the platform.

**Google Vertex AI & Agent Builder**

- **What it is:** Unified ML platform on GCP, with **Vertex AI Agent Builder** for no‑code/low‑code generative agents.[1][6]

- **Why it’s good:** Combines model hosting, fine‑tuning, retrieval, evaluation, and deployment with Google Cloud security and data services.[1]

- **Best for:** GCP shops that want managed agents tied into BigQuery, GCS, and Google’s infra.

**Amazon SageMaker (and AWS ecosystem)**

- **What it is:** AWS’s ML platform for training, hosting, and deploying models, with tools you can use to build agentic applications.[1][8]

- **Why it’s good:** Strong for custom models and deeply integrated with AWS services (Lambda, Step Functions, Bedrock, etc.).[1]

- **Best for:** Teams already standardized on AWS that want full control and scalability.

**Salesforce Einstein**

- **What it is:** AI layer tightly integrated into Salesforce CRM and Service Cloud, with low‑code agent capabilities.[1][6]

- **Why it’s good:** Native to Salesforce data, security, and workflows—ideal for sales/service agents and copilots in CRM.[1]

**IBM Watson Assistant**

- **What it is:** IBM’s conversational AI platform for chatbots and virtual assistants.[1]

- **Why it’s good:** Focus on enterprise deployments, call center integration, and compliance‑heavy industries.[1]

**When these are “best”**

- You’re an enterprise needing:

- Data residency, SOC2/ISO compliance, and identity integration

- Integration with cloud services (GCP/AWS) or line‑of‑business platforms (Salesforce)

- Centralized governance, monitoring, and cost control.

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## 4. Vertical and “agent as a product” tools

These are not just builders but ready‑made agents you can customize.

**Operator (OpenAI GPTs), Lindy, Devin, etc.**

- **OpenAI GPTs / Operator:** User‑configurable “agents” inside ChatGPT, very simple way to spin up tools‑using assistants and share them.[3][4]

- **Lindy:** Provides pre‑built agents for knowledge work (research, ops, support) with customization.[7]

- **Devin AI:** A specialized “software engineer agent” product that can autonomously work on coding tasks.[2][7]

Use these when you want outcome‑oriented agents with minimal building.

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## 5. How to choose the “best” stack for you

Here is a quick guide:

| Situation / Priority | Recommended tools |

|--------------------------------------------------------|-----------------------------------------------------------------------------------|

| Non‑technical team, need fast business agents | Glide, Voiceflow, Relevance AI, Make, Stack AI[1][2][3] |

| Product/ML team, custom logic & tools, multi‑agent | LangChain, CrewAI, AutoGen, Zep[5][6][7][2] |

| Deeply on AWS / GCP / Salesforce / IBM | SageMaker (with Bedrock), Vertex AI Agent Builder, Salesforce Einstein, Watson[1][6][8] |

| Already heavily using OpenAI APIs | OpenAI’s agent tools + LangChain or similar on top[4][6] |

| Want “done for you” agents with light config | GPT‑style agents, Lindy, Operator, specialist tools like Devin[2][3][7] |

If you tell me your tech stack (AWS/GCP/other), team skills (no‑code vs engineering‑heavy), and use cases (support, ops, coding, analytics, etc.), I can narrow this down to a very short recommended stack and deployment pattern.