Define AI output evaluation categories as catchable, predictable, or random to tailor your testing and feedback processes in domain-specific data pipe...Show more

A free online course covering tracing, testing & evaluation with Langsmith, and prompt engineering for AI application optimization.

The Deep Agents framework from LangChain, featured in the Academy course, underpins advanced agent engineering and course overviews.

Langsmith is used for loading datasets, tracing model runs, annotating or referencing gold-standard data, and evaluating AI application performance.

"Did you know there's an MIT study that showed 95% of failure?" — cited by Cameron to illustrate overemphasis on negative AI project outcomes.

Despite a widely cited MIT study claiming a 95% AI project failure rate, many companies report successful implementations when following structured op...Show more

Leverage historical logs and user thumbs-up/thumbs-down feedback to identify unhandled AI errors and drive targeted optimization within the applicatio...Show more

API-based AI integrations in traditional SaaS often fail to meet user satisfaction goals due to missing systematic test-and-feedback processes, not in...Show more

Many developer teams lack the internal practice of continuous optimization, which is essential for translating robust AI features into real customer o...Show more

Follow the LangChain Academy curriculum—starting with tracing, then testing and evaluation in Langsmith, and finally advanced prompt engineering—to sy...Show more

Adopt an AI engineering or agent engineering practice with offline, online, and real-time test optimizations to continuously gather feedback and refin...Show more

Build a backtesting framework using Langsmith to evaluate existing AI features, run model upgrade tests, and track performance changes over time.

The MCP server is cited as the current provider of live options market data used in the existing paper trading platform.

LangGraph is referenced as an agentic framework capable of abstracting automation loops and embedding decision trees into a cognitive architecture.

Offer a turnkey service that ingests and normalizes live options market data from sources like MCP server and exposes it via API for AI agent consumpt...Show more

Build a hosted LangGraph-based platform that lets users configure and deploy AI agents for automated options trading strategies.

High-frequency agentic deployments raise significant cost and speed challenges due to on-demand compute minutes and bursty workload patterns.

Options trading is now bot-driven at scale with bots competing against bots to capture premium every day, making it a prime AI application domain.

Feed real-time market data from the API into LangGraph and configure event alerts to trigger agent workflows.

Use Deep Agents in LangGraph to encapsulate and optimize high-frequency trading strategies.