At Mastra, we literally wrote the book on building AI agents. We've worked with hundreds of teams across industries to develop robust agent systems. Now, we're sharing a playbook to get you started.
Step 1: Scope the problem
To start, map problem specs to agent features and primitives.
Let's give a (slightly obscured) example of a government organization we've worked with. They needed agents to work with users to apply for, cancel, and check their benefits. As we talked with them, a few requirements emerged:
- Multi-agent orchestration capabilities to route tasks to specialized agents
- Processors to anonymize PII data
- Workflows with human-in-the-loop to guide users in an application process
Another company we worked with wanted to create an internal meeting recorder and send summaries to all attendees. This meant they needed:
- Speech-to-text capabilities to transcribe calls
- MCP integration to compose and send emails
- Scorers to evaluate the accuracy of summaries
Step 2: Pick a framework
Now you pick your framework.
If you're looking for a Typescript framework, we recommend Mastra :) If your team prefers Python, there are other options available.
Get a sense for what the frameworks support out of the box vs what you would have to build yourself. Especially if you're new to agent development, it's nice not to have to hand-roll a bunch of primitives.
Step 3: Sandbox with mock data
If you're building an agent that would require a lot of integrations and data sources, you may want to create some mock data matching the schema you would need in a first pass. This is especially true if the integrations are with bespoke, knotty systems. Alternatively, you can use a coding agent to create a minimal set of integrations and tools rather than the full suite.
For example, a Fortune 500 company uses Mastra agents for internal incident mapping. The prototype for this used CSV files to mock incidents and employee metadata.
An investment firm doing due diligence on potential deals mocked this with a data room of AI-generated PDFs, Excel documents, and contracts, a markdown file with a diligence checklist, and a one page memo as a text file.
Step 4: Test different architectures and models
Start using your framework to play around with your implementations. For your agent's tasks, play around with out-of-the-box tool calls vs. writing the code yourself. Also, tweak the agent architecture to see what works best.
One team building an internal chat bot tried a few different orchestration patterns and landed on having a supervisor agent focused on routing tasks with the sub-agents designed to handle small, scoped tasks.
Also, try different models. For example, you might use SoTA models like Claude Opus 4.6 / GPT 5.4 for coding tasks, and a cheaper, faster model like Gemini 3.1 Flash-Lite for simple classification tasks.
Step 5: Share your v1 with your team
To show a prototype, you would often build a custom frontend for your agent. With Mastra, you can share a link to our studio (either self-deployed or cloud hosted) with your teammates so they can interact directly with the agent without you having to build a frontend.
This is nice because then your teammates will just focus on agent quality rather than the nuances of how you're streaming responses or displaying tool calls.
The Mastra studio allows teammates to chat with their agent, visualize workflow runs, and see agent output in the form of evals and traces. It also includes an agent editor so that they can tweak prompts and tool descriptions.
Step 6: Figure out next steps to production
Obviously you have a great prototype, but there are a few more steps to getting it deployed to production. Typically this means:
- Observability: The tools to run traces, experiments, eval loops, etc. to evaluate and iterate on your agent
- Deployment: A reliable way to expose your agent in a UI, for both your developers and users
- Hosting: The infrastructure required to actually run your agent code, including tools, workflows, processors, and memory
If you're using Mastra, you can use our Server product to get your agent live on the Internet, and the Studio to handle observability.
Even if you're planning to eventually serve your agent from your own infrastructure, or use a different observability provider, it's nice to be able to iterate quickly while you're still prototyping.
Step 7: Build with confidence
Congrats!
You've scoped the goal, found the gaps, built the sandbox, validated the framework against a persistent-system lens, and gotten buy-in from your team.
Now go forth and build!
