Mastra Consulting

Mastra Consulting for Production AI Teams

Building with Mastra? I help teams design, evaluate, and ship reliable TypeScript agent systems.

Mastra is a useful framework. The harder question is whether your agent architecture, workflow boundaries, evals, observability, RAG, and memory design can survive real users. That is where I help.

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Fit

Who This Is For

This page is for teams that are already building or seriously evaluating Mastra. You do not need a generic AI transformation deck. You need production judgment on the system you are actually trying to ship.

  • You have a Mastra prototype and need to turn it into something your team can operate.
  • You are choosing an agent framework and want a senior review before the architecture hardens.
  • Your TypeScript team is building workflows, tool calls, RAG, memory, or human approval paths.
  • You need evals and observability before agent behavior becomes expensive to debug.
  • You want implementation help from someone who can work inside the codebase, not just advise from the side.

Problems

The Problems I Usually Find

The demo works, but the production shape is unclear

The agent can complete the happy path, but ownership, deployment, retries, error handling, and operational boundaries have not been designed yet.

Workflows are too loose

One agent is classifying, researching, scoring, routing, and drafting in a single loop. The system needs typed workflow steps, deterministic guardrails, and smaller model judgment points.

Evals and observability are missing

The team cannot tell whether agent quality is improving, regressing, or simply changing. Traces, evals, decision logs, and cost visibility need to exist before the system matters.

RAG and memory are underspecified

The agent has access to context, but retrieval quality, freshness, memory boundaries, and failure modes are not explicit enough to trust.

Approval and governance are prompt-shaped

Human review is described in the system prompt instead of enforced by the runtime, tool design, workflow boundaries, and traceable approval events.

How I help

How I Help

Mastra is the entry point. The work is production AI engineering: architecture, implementation, evals, observability, and the operating model around the agent system.

01

Architecture Review

Review the agent loop, workflow structure, tool boundaries, memory model, deployment shape, and ownership risks. You leave with a written assessment and a prioritized path.

02

Implementation Support

Hands-on work inside your TypeScript codebase: agents, workflows, tool schemas, structured outputs, approval gates, retrieval paths, and integration seams.

03

Eval and Observability Setup

Define what good output means, build the first eval loop, add traces and decision logs, and make quality visible enough to improve safely.

04

Workflow Design

Split broad agent behavior into typed workflow steps with clear contracts, deterministic code where it belongs, and model judgment where it adds signal.

05

RAG and Memory Design

Design retrieval and memory around the decisions the agent actually needs to make, including freshness, provenance, permissions, and failure handling.

06

Production Readiness Review

Identify what must be true before launch: rollback paths, monitoring, cost controls, human approval, data boundaries, and team ownership.

Engagements

A Focused Starting Point

Most team work starts with a Foundation Sprint: two weeks to inspect the current stack, identify the highest-leverage production gap, and ship one foundation piece. If there is a larger project, the Sprint can convert into an ongoing Production AI retainer.

Background

Why Me

  • 15+ years building production software, from senior IC work to CTO-level engineering leadership.
  • Former CTO at Buoy Software, where we shipped FDA-cleared medical device software.
  • Current hands-on production AI work, not just strategy or vendor selection.
  • Public Mastra and agent architecture content covering workflows, human approval, memory, tool use, and production constraints.
  • A practical bias toward systems your team can understand, evaluate, debug, and own.

Related work

Relevant Mastra And Agent Work

These are not case studies. They show how I think through agent architecture, workflow boundaries, and production controls.

FAQ

Common Questions

Do you only work on Mastra projects?

No. Mastra is a focused entry point for TypeScript teams building agents. The broader offer is production AI engineering: architecture, evals, observability, workflows, RAG, memory, and implementation support.

Can you help us decide whether Mastra is the right framework?

Yes. I can review the use case, team constraints, current stack, and production requirements before the framework decision hardens. Sometimes the right answer is Mastra. Sometimes the useful output is a clearer architecture decision.

Can you write code with our team?

Yes. Foundation Sprints and retainers can include hands-on implementation in your codebase. I am most useful when architecture review and implementation feedback happen against real code.

What should we have before booking a call?

A clear description of what you are building, why Mastra is on the table, and where the current uncertainty is. A prototype, repo walkthrough, trace, eval sample, or architecture sketch helps, but it is not required for the intro call.

Is this for beginners learning Mastra?

Not usually. This is for engineering teams, technical founders, and senior engineers trying to ship reliable agent systems. If you are just learning the framework, my public videos and articles are the better starting point.

Building with Mastra and hitting production questions?

Book a free 30-minute intro call. We will talk through what you are building, where the risk is, and whether a Foundation Sprint or retainer is the right next step.

No pitch deck. No pressure. Just a technical conversation about the system.

Schedule an intro call