The hype-mongers who create television commercials for big AI vendors make it seem like AI agents will do everything we ask and more. They’ll anticipate our needs, process the data, spruce up everything, and take out the trash. Well, that last part is left for you.
Imagining a life of working from a hammock is easy. Making the dream come true requires, alas, work and technology. Yes, the companies specializing in building agent workforces eat their own dogfood, so to speak, and include agentic superpowers inside their products, but that doesn’t mean agentic workflows can be tossed together with a quick prompt spoken while eating peeled grapes on the lanai.
This is where agentic orchestration tools come in. A number of companies are building sophisticated tools for managing teams of agents. You bring your dreams, and their tools handle the clerical work of connecting data pipelines to the right models and then back to the tables where output data is stored. If demand spikes, they’ll handle the scaling for you.
What follows is an overview of platforms available today for orchestrating AI agents. Some are no-code tools aimed at helping non-developers create sophisticated apps with handwavy text. Others are hybrid solutions that help developers move faster. The tools generate a week’s worth of work with just a bit of talk and a few smart lines of human-written code.
Each platform is capable of creating algorithms and workflows that break down tasks into parts that are then fed into a big collection of models before collecting the results. If there’s a need to create loops and readjust the progress midstream, they can often do that, thinking both tactically and strategically.
When they work well, and they often do, the results can be surprisingly powerful and liberating. The agent teams create plans, break them into parts, and then attack them. They curate the data and orchestrate the flows to handle complex, multistep projects.
When they don’t, well, the human gets to pick up the pieces. Many deliberately include humans by eliciting sign-off at specific junctures. Regardless of how amazing a system might appear to be, it would be foolish for any of us to expect perfection. It’s better to think of them as important and necessary platforms for experimentation. And the best way to experiment is to fire them up.
Here is an alphabetical list of 21 tools that will boot up a collection of agents and care for them as they work together and independently on the tasks they’ve been assigned.
Agentforce
The Agentforce platform from Salesforce is designed to add a layer of AI to their business support software. The agents are constructed in Builder using the special Agent Script. Business logic is kept separate and executed in sequence through traditional computing to avoid any of the hallucinations and outright fabrication that come from large language models. The LLMs add a layer of natural conversation and, if necessary, vocalization. Their main use is leveraging their traditional market sectors for supporting the sales process.
AWS Bedrock AgentCore
Any team that relies heavily on AWS staples like Lambda can rely on AgentCore to handle the meta-chores for keeping sites up and running. Agents and agent groups are designed to integrate first with AWS. It is possible to use glue code to involve other clouds, but the company designed the platform to support its general infrastructure with a serverless model that is strictly pay-as-you-go. The product leverages all the various serverless options built into the AWS cloud while also maintaining a state of its own. A collection of dashboards makes it possible to track and debug agent actions.
BigPanda
The folks at BigPanda use the initialism AI to stand for “alert intelligence.” Their system monitoring software gathers details about overloading, failures, and bottlenecks. Then the AI makes sense of these alerts by normalizing and enriching them with added context and by constructing a history. After all this, the flood of alerts is boiled down to a few actionable events that can be more easily understood.
CrewAI
The CrewAI platform supports building and deploying agents that can, when needed, work together in swarms or crews. The platform supports agents’ information gathering while tracking all behavior with traces, logs, and metrics so debugging is possible. CrewStudio is used to build the agents, via Python or a bit of vibe coding. Then they’re deployed to the CrewAI AMP, which monitors their progress and flags any errant or sluggish behavior. Hosted versions have a free tier and a paid tier. Open source versions are readily available.
Devin AI
The tool from Devin.AI is designed to be an autonomous software engineer that can work at every layer. It constructs a list of tasks by poring over tickets in integrated systems such as Jira, Slack, Teams, and Linear. Then it constructs a plan. When you approve the plan, Devin will refactor the code as needed, construct tests, and check whether the new code passes them. Some customers deploy it to clear backlogs of bugs. Others insert Devin into the CI/CD pipeline to handle specific chores such as maintaining test sets or documentation. The platform is designed to be as easily deployed as a human engineer.
DynaTrace
The AI from DynaTrace (Davis AI) is called a “causation agent” because the company wants to emphasize its role in unpacking and explaining why a software stack is failing. It takes reports of degraded performance or failures and generates reports that determine the root cause so it can be fixed. The AI can dive into the code and network topology to make better informed conclusions. After that, Davis CoPilot can help developers and DevOps teams execute fixes.
Griptape
The visual node builder from Griptape assembles a team of agents for managing a data pipeline. Its cloud handles all deployment and scaling of the applications built with Griptape’s framework. One feature called “off prompt” refers to the way its framework can process large blocks of data efficiently by putting only the most relevant parts in the prompts fed to the LLM. Data is stored in a database that can be queried by the LLM as needed, potentially saving large amounts of money on computation. Its modular Python framework is also available under an Apache 2.0 license.
Kubiya
DevOps teams will be interested in Kubiya because its first role is integrating with a standard cloud environment and handling many of the standard chores such as deploying new instances or reconfiguring a cloud architecture. It integrates with tools such as Slack so that the team can treat it like another team member that can create plans, analyze those plans, and then actively deploy them when the time comes. They’ve explicitly focused on delivering a team of agents that acts deterministically — that is, when a plan is ready, the platform will not inject randomness into the execution.
LangGraph
If your workflow is set up as a complex graph with loops and feedback, LangGraph is a good option. The tool is designed to enable different agents and models to work independently with the assurance that the LangGraph mechanism will handle the larger chores of ushering the workflow to completion. If tasks need to be repeated, the graphs can be cyclic. Other tools such as LangSmith and LangChain are designed to rely on LangGraph for task-wide state coordination. Open-source code is available under an MIT license.
LlamaIndex
The LlamaIndex project began as a vector search tool, but it’s grown to support hosting agents directly. They can iterate on the data that’s stored by working cooperatively with the index. The tool, which is mainly programmed in Python or TypeScript, includes extensive debugging support that can also help bring humans into the loop. The full open-source code is available under the MIT license.
LogicMonitor
LogicMonitor’s AI agent Edwin integrates with a wide range of enterprise monitoring tools so any issues can be correlated and contained. The agentic approach enables it to poll the trouble spots and develop potential solutions. Humans can converse with Edwin in natural language, a feature that effectively merges the decision-making power of the human with the broad intelligence-gathering ability of Edwin. The goal is to create a set of plans that heal any anomalies in your enterprise architecture.
Microsoft AutoGen and Semantic Kernel
Microsoft released its AutoGen framework to encourage users to build their own crews of LLM agents. The agents, written in your choice of Python, .Net, or a few other popular programming languages, rely on the framework for asynchronous messaging. The system tracks dataflows for observability and debugging. Pre-written extensions handle many common scenarios such as supporting an MCP server. The Semantic Kernel is another similar project that works with various models and different languages such as C#, .Net, and Java. A similar plugin ecosystem opens up MCP servers, vector databases, and OpenAPI options. Open-source code is available here and here.
N8n
The team at n8n likes to say its tool lets you code when you feel like it and lets the AI step in when you don’t. A visual workflow editor enables you to string together multiple agents and then chat with them as needed. The tool can either leverage commercial models or work with self-hosted models that work on-prem. Some of the stack is available with a Sustainable Use license.
PagerDuty
Bad news is a bit easier to take from the agents in the PagerDuty cloud. Instead of just announcing the problem, the agents from PagerDuty now have a mandate to construct plans and follow them through until the problem is resolved. The agents are focused entirely on understanding the connection between bad events and the solutions needed to fix them. There are connections with more than 700 infrastructure tools, from repositories to cloud providers.
Prefect
Teams of agents can synchronize their behavior using the state machines from Prefect. The Python-centric tool was originally designed to choreograph data science workflows and has since expanded to enable individual agents to take over tasks. Developers who want to offer an MCP gateway can turn to FastMCP and MCP Horizon to handle the chores of regulating access.
Pydantic AI
Python developers who rely on the data validation and cleansing power of Pydantic can now add Pydantic AI to their project, too. The type-safe approach can add more structure and robustness to the AI development process. The framework connects with all the major MCP or Agent2Agent specifications so event-based communication can coordinate the agents. The general-purpose telemetry gathering platform, Logfire, ensures that any person or AI doing the debugging has plenty of data. Available via the MIT license.
Relevance AI
The templates from Relevance AI help jumpstart anyone who wants to build an agentic workflow in their system. They’re aimed mainly at marketing, customer support, and sales. Their prospect researcher, for instance, will pull information from a variety of integrations so that the sales force can go into any meeting fully briefed with data about the prospect. Start with a template and then iterate through multiple debug cycles until it’s ready for deployment.
ServiceNow
The main role for ServiceNow was once about supporting customer service departments. The platform’s agentic layer can now automate some of the most common tasks, there and elsewhere in the enterprise. It is designed to provide a unified response for many of the tasks involved in network governance, HR, and IT management. ServiceNow offers a range of tools, including AI Agent Studio and AI Control Tower, designed to nurture a collection of agents that do more than just offer a better chatbot. They will be able to act quickly when desired.
Strands Agent
The framework from Strands Agent can support many of the swarm architectures using either Python or TypeScript. The default examples rely on AWS tools such as Bedrock, and it is popular with cloud engineers who use agentic capabilities and LLMs to curate information flows across cloud instances. Many of the examples show how to build tools to handle cloud-centric tasks such as synthesizing multiple data sources or building repetitive jobs with a cyclic structure with just a few lines. The tool supports all major clouds, including AWS, Azure, and GCP. Some of the tools and APIs are released with Apache license.
Temporal
Teams with a need for long-running, distributed workflows can turn to Temporal, a system with fail-safe orchestration and persistent state. AI applications that coordinate multiple agents can work alongside other data processing in pipelines. Temporal will capture state at each step ensuring that a failed prompt or an errant process won’t upend the entire process. When parts fail, Temporal restarts it. Available as open source or as a service.
Vellum
Developers who are iterating on various agentic solutions turn to Vellum to support the cycle of creation. It’s an IDE that’s optimized for tracking AI agent behavior. The data flows from all the various prompts, and answers are available to help track when the solution is working and when it is failing. Extended regression testing and version control ensure that teams can track their progress without introducing bugs.