The 10 Hottest Agentic AI Tools And Agents Of 2025 (So Far)

Here are 10 of the hottest agentic AI tools and agents launched in 2025 from the likes of AWS, Databricks, Dataiku, Google Cloud, GitHut, IBM, Salesforce, ServiceNow and Snowflake.

From new Amazon Web Services and Dataiku AI agents to Google Cloud’s Conversational Agents Console and GitHub’s new Coding Agent for Copilot, the amount of agentic AI innovation launched during the first half of 2025 is astounding.

The largest and most innovative AI companies in the world, including Nvidia, Microsoft and Salesforce, unveiled a significant amount of agentic AI technology to their global customers and channel partners in 2025 that’s shaping the AI era.

Before jumping into the 10 hottest agentic AI tools and agents launched in 2025, here’s a quick look at the market forecast ahead.

Global Agentic AI Market To Reach $127 Billion

The global agentic AI market is expected to grow from $28 billion in 2024 to $127 billion by 2029, according to tech research firm Market Research Reports, representing a compound annual growth rate of 35 percent.

IT research company Gartner says, by 2029, agentic AI will autonomously resolve 80 percent of common customer service issues without human intervention, leading to a 30 percent reduction in operational costs.

[Related: The 10 Hottest AI Startup Companies Of 2025 (So Far)]

While previous AI models were limited to generating text or summarizing interactions, agentic AI and AI agents introduce a new paradigm where AI systems have the capability to act autonomously to complete tasks.

“Agentic AI has emerged as a game-changer for customer service, paving the way for autonomous and low-effort customer experiences,” said Daniel O’Sullivan, senior director analyst for Gartner, in a recent report. “Unlike traditional GenAI tools that simply assist users with information, agentic AI will proactively resolve service requests on behalf of customers, marking a new era in customer engagement.”

CRN breaks down the 10 coolest agentic AI products, tools and agents in 2025 so far which include new offerings from AWS, Databricks, Dataiku, Google Cloud, GitHub, IBM, Salesforce, ServiceNow and Snowflake.

AWS Strands Agents

AWS’ new Strands Agents is an open-source SDK that takes a model-driven approach to building and running AI agents in just a few lines of code.

Strands scales from simple to complex agent use cases, and from local development to deployment in production.

Compared with frameworks that require developers to define complex workflows for their agents, Strands simplifies agent development by embracing the capabilities of models to plan, chain thoughts, call tools, and reflect.

With Strands, developers can define a prompt and a list of tools in code to build an agent, then test it locally and deploy it to the cloud. Strands connects two core pieces of the agent together: the model and the tools. Strands plans the agent’s next steps and executes tools using the advanced reasoning capabilities of models.

Databricks Agent Bricks

In June, Databricks launched Agent Bricks, a new unified workspace for building production-scale AI agents that works with an organization’s unique data to achieve accuracy and cost efficiency.

The new agentic AI provides an automated way to create high-performing AI agents tailored to a business with developers providing a high-level description of the agent’s task and connect it to enterprise data.

Agent Bricks automatically generates task-specific evaluations and LLM judges to assess quality and creates synthetic data to substantially supplement the agent’s learning. It then searches across the full gamut of optimization techniques to refine the agent.

The offering is designed to overcome several common problems around agentic development, including organizations’ lack of enough data to build agents and difficulty evaluating how well they are working once in production.

Agent Bricks builds on technology Databricks acquired when it bought generative AI startup MosaicML in 2023 for $1.3 billion.

AI Agents With Dataiku

Dataiku this year debuted AI Agents, a new set of capabilities within Dataiku’s Universal AI Platform for creating and controlling AI agents at scale.

Within AI Agents is the Dataiku LLM Mesh architecture to manage model access across all proprietary, open-source and cloud service LLMs. Additionally, Dataiku Safe Guard defines and applies guardrails while Agent Connect centralizes agent access across an organization from a single interface.

The platform supports the central creation of agents with Code Agent for data scientists and developers and the Visual Agent no-code option for non-technical business users. Capabilities include Managed Agent Tools for maintaining the quality and validation of tools used by agents and a GenAI Registry for strategic oversight of agentic use cases.

For agent observability and performance monitoring, AI Agents with Dataiku provides Trace Explorer for visibility into agent decision making, Quality Guard to continuously evaluate and monitor agent performance, and Cost Guard for real-time usage tracking, budget enforcement and internal cost allocation.

Google Cloud’s Conversational Agents Console

Google Cloud has launched a new unified console for building AI agents that combines generative AI and rules-based controls to enable rapid building of AI agents with realistic, natural-sounding inflection and expressive conversations for self-service experiences.

Google’s Conversational Agents uses the latest Gemini models, enabling human-like, high-definition voices, comprehension, and the ability to understand emotions—so the AI agents can adapt during conversations.

The new Conversational Agents console provides new evaluation capabilities to benchmark agent performance and improve reliability and quality at scale.

“We also introduced observability, evaluation and test-case instrumentation into the Conversational Agents console,” said Antony Passemard, director of product management and Applied AI at Google Cloud in a blog post this year. “This enables customers and partners to validate agent quality at scale, with tools to consistently monitor and improve their self-service experiences.”

IBM AskIAM

IBM’s new AskIAM agentic AI capability built on IBM watsonx Assistant that helps users modernize their identity and access management (IAM) systems.

AskIAM can be used to simplify identity management processes like provisioning and access requests while also providing advanced intelligence and automation to strengthen identity protection.

With an open architecture design, AskIAM can leverage existing customer investments in LLMs, retrieval-augmented generation (RAG), and messaging middleware.

“The need for automated identity and access management to reduce manual effort is long overdue,” said IDC Research Manager Scott Tiazkun in a recent statement. “IBM’s AskIAM uses AI to streamline access requests and reduce delays via integration with Microsoft Teams and Slack for notification and action. The AI assistant helps users, auditors, and executives get faster, easier access and better visibility into compliance and security. As an agent for identity, AskIAM delivers one of the most user-friendly experiences in the space.”

Microsoft’s GitHub Coding Agent For Copilot

Microsoft’s GitHub Copilot now includes an asynchronous coding agent, embedded directly in GitHub and accessible from VS Code—creating a powerful Agentic DevOps loop.

The agent can autonomously refactor code, improve test coverage to fix defects and even collaborate with other agents on more complex tasks.

The Copilot coding agent operates within GitHub’s native control layer, built in the flow of the software development life cycle.

As the agent works, it pushes commits to a draft pull request, and users can track it every step of the way through the agent session logs. Developers can give feedback and ask the agent to iterate through pull request reviews.

The agent has built-in security features like branch protections and controlled internet access to ensure safe and policy-compliant development workflows. Additionally, the agent’s pull requests require human approval before any workflows are run, creating an extra protection control for the build and deployment environment.

The agent is available to Copilot Enterprise and Copilot Pro+ users.

Nvidia NeMo Agent Toolkit

Nvidia’s new NeMo Agent toolkit is an open-source library that provides framework-agnostic profiling and optimization for production AI agent systems.

By exposing hidden bottlenecks and costs, it helps customers scale agentic systems efficiently while maintaining reliability.

The agentic AI toolkit provides unified monitoring and optimization for AI agent systems, working across LangChain, CrewAI, and custom frameworks. It captures granular metrics on cross-agent coordination, tool usage efficiency and computational costs, enabling data-driven optimizations through Nvidia’s accelerated computing.

It can be used to parallelize slow workflows, cache expensive operations, and maintain system accuracy during model updates.

The Agent toolkit supports the Model Context Protocol (MCP) letting developers use the toolkit to access tools served by remote MCP servers or as a server to make their own tools available to others via MCP.

Salesforce Agentforce 3

Salesforce’s new Agentforce 3 agentic AI solution provides customers with more visibility and control to scale AI agents by enabling seamless agent interoperability with built-in support for open standards like Model Context Protocol (MCP).

With a new Command Center for complete observability and over 100 new prebuilt industry actions to speed time to value, Agentforce 3 helps companies scale what works, fix what doesn’t, and unlock the full potential of agentic AI.

Agentforce 3 delivers an updated Atlas architecture—from reasoning, to performance, to trust—for enterprise readiness including lower latency, greater accuracy, enhanced resiliency, and support for natively hosted LLMs such as Anthropic.

“We’ve unified agents, data, apps, and metadata to create a digital labor platform, helping thousands of companies realize the promise of agentic AI today,” said Adam Evans, general manager of Salesforce AI, in a statement in June. “Agentforce 3 is a major leap forward for our platform that brings greater intelligence, higher performance, and more trust and accountability to every Agentforce deployment.”

Through an expanded AgentExchange, customers can access plug-and-play services from over 30 vendor partners including AWS, Box, Cisco, Google Cloud, IBM, PayPal and Teradata.

ServiceNow AI Agent Orchestrator

ServiceNow’s new AI Agent Orchestrator ensures teams of specialized AI agents work together across tasks, systems, and departments to achieve a specific goal.

It contains thousands of pre‑built agents across IT, customer service and HR, plus a new AI Agent Studio for building fully customized agents.

This new addition on the ServiceNow Platform acts as the AI agent control tower—one central location to analyze, manage, and govern the rapidly evolving world of agentic AI across every corner of a business.

The AI Agent Orchestrator enables inter‑agent communication and centralized coordination. This ensures AI agents can efficiently share information and hand off tasks regardless of where the process starts.

“In a future with millions of AI agents acting as your new digital workforce, ServiceNow serves as the AI agent control tower, bringing order to chaos,” said Amit Zavery, president, chief product officer and chief operating officer at ServiceNow, in a statement.

Snowflake’s Data Science Agent

At its Snowflake Summit 2025 in June, Snowflake unveiled Data Science Agent, which the company said is an agentic AI companion that boosts data scientists’ productivity by automating routine machine learning model development tasks.

Snowflake’s new Data Science Agent simplifies AI and ML workflows, democratizes users’ access to data across their businesses, and eliminates technical overhead—all through a natural language interface within Snowflake.

Data Science Agent uses Anthropic’s Claude large language models to break down problems associated with ML workflows into distinct steps, such as data analysis, data preparation, feature engineering and training.

The agentic AI tool creates fully functional pipelines using such advanced techniques as multi-step reasoning, contextual understanding and action execution.