Understanding AI Orchestrators, Agents, Tools, and Workflow Automation

Introduction

AI is incredibly powerful when it comes to generating responses based on existing knowledge. However, when AI is asked for real-time information—such as checking the status of a GitHub pull request, retrieving pending tasks from Asana, or fetching the latest data from a live SQL database—it cannot provide an answer on its own. To solve this, AI systems rely on three key components: Tools, Agents, and an Orchestrator. These components work together to enable AI to interact with real-world systems dynamically, automating workflows efficiently.

Tools: The Hands and Eyes of AI

Tools are simply functions with a description of their purpose, enabling interaction with external systems like GitHub, Asana, and Figma. They serve as AI's hands for manipulating data and eyes for retrieving information from various sources.

AI itself cannot directly communicate with GitHub, Asana, or an SQL database. Instead, it uses tools to bridge this gap. Examples of Tools:

• GitHub API – Fetches details about repositories, issues, commits, or pull requests.
• Google Calendar API – Fetches upcoming meetings or schedules.

Example Scenario:

• 👉 A developer asks:❓ "What are the open pull requests in my GitHub repository?"
• 👉 The GitHub API tool is used to query the repository and return a list of open PRs.
• 🔹 Without tools – AI wouldn't be able to fetch real-time GitHub PRs.
• 🔹 With tools – AI can dynamically pull the latest PR list and provide an accurate response.

Agents: The Managers of Tools

Agents act as domain experts that manage specific tools. Each agent specializes in a particular area and knows which tool to use and how to use it efficiently. Examples of Agents:

1. Dev Agent – Responsible for handling developer-related queries.
   • Uses GitHub API to check issues, PRs, or repository status.
2. PM (Project Manager) Agent – Manages project-related tasks.
   • Uses Asana API to track pending tasks, create tasks etc.

Example Scenario:

• A project manager asks:❓ "What are the pending tasks in our Asana project?"
• 👉 The PM Agent receives the request and determines that Asana is the right tool to fetch project tasks.
• 👉 It queries the Asana API, retrieves the task list, and returns it to the user.
• 🔹 Without agents – AI wouldn't know which tool to use or how to structure the query.
• 🔹 With agents – The correct tool is automatically selected based on the request type.

Orchestrator: The Brain That Manages Agents

The orchestrator acts as the decision-maker that manages all agents and ensures everything runs smoothly.

Whenever a user submits a request, the orchestrator:

• ✅ Understands the query and determines which agent can handle it.
• ✅ Routes the request to the appropriate agent.
• ✅ Collects the agent’s response after it queries the tool.
• ✅ Formats and returns the final result to the user.

Why Do You Need an Orchestrator?

When automating a workflow that relies on multiple tools, it's important to use specialized agents for different tasks. If a single agent has access to all tools, it may get confused or make mistakes when choosing the right tool. To avoid this, we assign each agent a specific area of expertise. An orchestrator is then needed to manage these agents, ensuring the right agent is selected for each task and coordinating their interactions smoothly.

For example, a complex business process might involve:

• The Dev Agent retrieving the latest GitHub pull requests.
• The PM Agent checking pending tasks in Asana.

Instead of handling these steps manually, an orchestrator coordinates everything dynamically, ensuring seamless execution.

Example Scenario: Automating a Multi-Agent Workflow

A user asks:❓ "Can you show me the latest pull requests and review them? If the PR is good, merge it and mark the ticket in Asana as Done."

How the Orchestrator Handles This Request:

1️⃣ The orchestrator identifies that this request requires collaboration between multiple agents:

• Dev Agent (for GitHub actions: fetching, reviewing, and merging PRs).
• PM Agent (for updating Asana tasks).

2️⃣ The Dev Agent queries the GitHub API to fetch the latest open pull requests.

3️⃣ The Dev Agent then analyzes the PRs using an AI-based review tool to check code quality, adherence to guidelines, and test coverage.

4️⃣ If a PR passes review, the Dev Agent merges it into the main branch using the GitHub API.

5️⃣ The PM Agent identifies the corresponding ticket in Asana linked to the merged PR.

6️⃣ The PM Agent updates the task in Asana, marking it as "Done."

7️⃣ The orchestrator gathers all responses from the Dev and PM Agents and presents the final status back to the user.

🔹 Without an orchestrator – The Dev Agent and PM Agent would act separately, and the user would have to manually coordinate their actions.

🔹 With an orchestrator – The entire workflow is automated, ensuring a smooth, structured, and efficient process. 🚀

Conclusion

In the rapidly advancing world of AI-driven automation, orchestrators, agents, and tools are essential for enabling AI to move beyond static responses and interact dynamically with real-world systems.

📌 Tools act as a bridge between AI and external services, enabling it to retrieve real-time information and execute tasks.

📌 Agents function as domain specialists, identifying the right tools and using them effectively.

📌 The Orchestrator ensures smooth coordination among agents, optimizing complex workflows for efficiency and automation. By adopting this structured framework, businesses can enhance productivity, minimize manual effort, and streamline operations seamlessly.