Aron Schuhmann
Head of Demand Generation
Aron Schuhmann is the Head of Demand Generation at Mastra. A career-long B2B SaaS marketer, he has worked at the intersection of AI and developer tools since 2015, serving as an early growth and demand-generation hire at MightyAI (acquired by Uber), Gatsby (acquired by Netlify), and OctoAI (acquired by NVIDIA).
Posts by Aron Schuhmann
AI agents for business: what they automate, where they fail
Learn what AI agents for business can reliably automate, where they fall short, how to deploy them safely, and how to test agents in production.
Best AI tools for business in 2026: tested and ranked
Compare the best AI tools for business by category, from chatbots and analytics to agent frameworks, with honest strengths, trade-offs, and pricing.
The AI agent stack: layers, tools, and how to build in 2026
A developer’s guide to the AI agent stack in 2026: models, tools, memory, frameworks, evals, guardrails, and deployment, with real tradeoffs at each layer.
LLM as a judge: how to evaluate LLM and agent outputs at scale
Learn what LLM as a judge is, how to build a reliable LLM judge, avoid bias, and evaluate agent outputs at scale with prompting and calibration methods.
Agent builder: how to build, test, and deploy AI agents
Learn what an AI agent builder is, the core building blocks of an AI agent, and how to build, test, and deploy your first agent to production.
AI agents news: latest developments, adoption trends, and what’s next
AI agents news, adoption trends, use cases, and production challenges. Learn where agentic AI stands and what developers should watch next.
CrewAI multi-agent framework: architecture and use
Learn how the CrewAI multi-agent framework works, how to build and test crews, and how to run production multi-agent systems.
Your open source LLM guide for 2026: models, benefits, and deployment
Compare the best open source LLM models in 2026, how licensing works, deployment tradeoffs, and how to evaluate and run open models in production.
Context engineering for AI agents: a practical guide
Learn what context engineering is, how it differs from prompt engineering, and the strategies for managing context in production agent systems.
Generative AI models: types and how they work
Learn what generative AI models are, the main types of generative AI models, how they work, and how to build and evaluate them in TypeScript.
Generative AI tools: what they are and how they work
Explore the main types of generative AI tools, real examples like ChatGPT and Gemini, and how developers build production apps with models, RAG, and evals
Multi-agent systems: architectures, frameworks, and real-world applications
Learn how multi-agent systems work, compare architectures and frameworks, and see how to build, debug, and evaluate multi-agent AI systems in production.
Long-term memory for AI agents: what it is and how to build it
Learn what long-term memory means for AI agents, why larger context windows fall short, which memory types matter, and how to design persistent retrieval.
LangGraph: the complete guide to stateful AI agent orchestration
Learn what LangGraph is, how its graph-based architecture handles stateful AI agents, and when to choose it over LangChain for complex workflows.
LLM leaderboard: how to read, compare, and use benchmark rankings
Learn how to read LLM leaderboards, compare benchmark scores across reasoning, coding, and safety, and run your own evals to pick the right model.
Embeddings in machine learning: what they are, how they work, and when to use them
Learn what an embedding is, how embedding models work, and how to use embeddings for semantic search, RAG pipelines, and AI agent memory in production.
Guardrails for AI agents: a practical guide for TypeScript developers
Learn how to implement guardrails for AI agents in TypeScript, from input validation and PII detection to tripwires, classifiers, and observability.
AI automation tools in 2026: a practical guide for every use case
Compare the best AI automation tools for workflows, agents, content, and meetings. Honest breakdowns with strengths, trade-offs, and use-case verdicts.
AI hallucination: what it is, why it happens, and how to reduce it
Learn what an AI hallucination is, why language models produce false outputs, and practical techniques to detect and reduce hallucinations.
AI assistants: how they work, what they do, and where they’re headed
Learn what AI assistants are, how they work, the key types and use cases, and how they compare to AI agents. Practical guide for teams building with AI.
Agentic AI tools: a complete guide for teams building with autonomous AI
Learn what agentic AI tools are, how they differ from traditional automation and generative AI, and how to build and evaluate autonomous AI agents.
Best AI agents in 2026: tested, compared, and ranked
Compare the best AI agents in 2026 for reasoning, coding, research, enterprise workflows, and multi-agent systems, with practical selection guidance.
Agent swarm: what it is, how it works, and how to build one
Learn what an agent swarm is, how multi-agent architectures work, and how to design, build, and observe production-ready agent swarms in TypeScript.
Best LLM for coding: a developer’s guide to top models in 2026
Compare the best LLMs for coding in 2026 across performance, context, cost, privacy, and production fit, with practical model-selection guidance.
LLM evaluation: frameworks, metrics, methods, and best practices
Learn how to evaluate LLMs with proven metrics, benchmarks, LLM-as-a-judge methods, and agent evaluation strategies for development and production.
Trace ID explained: what it is, how it works, and when to use it
Learn what a trace ID is, how it differs from a correlation ID, and how to implement distributed tracing with OpenTelemetry in your services.
AI agent examples: Real-world use cases across industries
Explore real-world AI agent examples across finance, healthcare, retail, and more. Learn agent types, production patterns, and how to build your own.
Generative AI vs predictive AI: What’s the difference?
Your guide to generative AI vs predictive AI: how each works, real use cases, tradeoffs, and how to choose the right approach.
AI agent orchestration: patterns, architecture, and implementation
Learn AI agent orchestration patterns, architecture, implementation steps, observability practices, risks, and framework choices for production systems.
How to create a chatbot: a complete guide for developers
Learn how to create a chatbot with LLM APIs. Covers the request loop, prompt design, tool calling, RAG, deployment, and production observability.
Workflow orchestration: a complete guide
Learn what workflow orchestration is, how it compares to automation, common patterns like DAGs and state machines, and how to implement it for AI agents.
Agent memory platform: how AI agents store, retrieve, and use context
Learn how an agent memory platform stores, retrieves, and applies context across sessions. Covers memory types, retrieval pipelines, tools, and evaluation.
Agent memory: types, techniques, and implementation guide
Learn how agent memory works, from short-term buffers to observational recall. Explore types, engineering patterns, and production implementation strategies.
Agent systems: architectures, patterns, and how to build for production
Learn how agent systems work, when to use single vs. multi-agent architectures, and how to build agent systems with context engineering and observability.
Agentic RAG: how it works, core architectures, and production tradeoffs
Learn how agentic RAG improves on traditional retrieval with multi-hop reasoning, self-correction, and production-ready orchestration in TypeScript.
Agentic workflows: how they work, key components, and how to build them
Learn how agentic workflows work, what components you need, and how to build one step by step, including observability, testing, and real-world use cases.
How to build AI agent evaluation that ships reliable agents
Choose metrics, build graders, run evals in CI/CD, and monitor production to catch failures before your users do.
AI agent framework: a practical guide to choosing and using the right one
Compare AI agent frameworks, learn key selection criteria, and explore TypeScript patterns for agents, workflows, memory, and observability.
AI agent hosting: options, setup, and deployment for production
Compare AI agent hosting options from serverless to containers. Learn how to deploy, observe, and scale TypeScript agents in production.
AI agent observability: a complete guide for production teams
Learn how AI agent observability works, its core pillars, best practices for tracing and evals, and how to monitor agents reliably in production.
AI agent workflows: a complete guide for developers
Learn how AI agent workflows work, common patterns like routing and parallelization, and how to build, evaluate, and monitor agentic workflows in production.
AI agents: what they are, how they work, and how to build with them
Learn what AI agents are, how they reason and act with tools, the five classical types, and practical guidance for building, testing, and deploying your own.
AI gateway: one integration point for every provider
Learn what an AI gateway does, how it routes requests across providers, and how to implement one for cost control, observability, and reliability.
AI workflow automation: how to build reliable pipelines that handle complexity at scale
Build AI workflow automation that handles complexity at scale, with branching, conditions, and human-in-the-loop checkpoints that adapt where rules can't.
AI workflows: what they are, how they work, and how to build them
AI workflows combine LLMs, tools, and orchestration logic to automate complex tasks. Learn key components, use cases, and how to monitor them.
How to build AI agents: a practical guide for developers
Learn how to build AI agents in TypeScript with practical patterns for tool design, guardrails, evals, and production-ready deployment.
LangChain alternatives: the best frameworks for LLM development in 2026
Compare the best LangChain alternatives for 2026, from AI agent frameworks and RAG tools to enterprise platforms and direct LLM access.
LLM observability platform: a complete guide for AI teams
Learn how to instrument, monitor, and evaluate LLM apps in production with traces, metrics, evals, and the right observability platform for your stack.
RAG chatbot: a complete guide for TypeScript developers
Learn how to build a RAG chatbot in TypeScript, from embeddings and chunking to retrieval, evals, and deployment with Mastra.
RAG framework guide: how retrieval-augmented generation works and which tools to use
Learn how a RAG framework connects LLMs to external data. Covers RAG architecture, tools like LangChain and LlamaIndex, and how to build a pipeline.
RAG platforms: what they are, how they work, and how to choose one
Learn what a RAG platform does, how RAG pipelines work end to end, and what to evaluate when choosing one for production AI agents.