Use a multi-tenancy architecture to let managed service providers host isolated AI/LLM instances for multiple clients with unified permission, risk, a...Show more

Implement a constrained collaboration framework over shared datasets or APIs by embedding access controls into AI workflows for chat, development, or ...Show more

Modular microservices with security controls and network transport form a new generation infrastructure for AI agent layers enabling tool and skill or...Show more

Host Python-based AI services using Docker containers on Azure Container Apps for quick deployment and scalability.

Implement an orchestrator agent pattern with subagents in Claude Code to modularize trading analysis and recommendation tasks.

Utilize the Polygon API within a raw AI trading agent to fetch live quotes and integrate them into trading analysis workflows.

Integrate a public market data API (like Massive) with Claude or OpenAI to flesh out, validate, and backtest options trading strategies programmatical...Show more

Implement a "build portfolio" framework to systematically track and evaluate projects you've publicly discussed, serving as a structured deal pipeline...Show more

Implement a process to manage and define acronyms in AI outputs to prevent agents from inventing inconsistent taxonomies.

Establish a core glossary and taxonomy for AI agent terms to standardize definitions across software and avoid synonym conflicts.

Implement AI gateways with sandboxed environments to safely integrate and manage Claude skills deployments in production infrastructure.

Use Docker to containerize a deep agent as an MCP server for remote connections and running local GPT OSS models on high-end hardware.

Use win/tie/loss metrics to compare agentic performance of AI models and compute average cost per task to inform cost-optimization decisions.

Use GPT-5 Pro to generate reusable Python code artifacts along with execution results, then validate and iterate in your own environment.

Wrap open-source LLMs like Minimax M2 in custom agent shells and integrate them into your local or cloud dev environment for rapid prototyping.

LangChain’s new Agent Builder simplifies building custom AI agents by integrating tools, prompt templates, and execution workflows.

The Prompt Hub feature in LangChain supports an iterative prompt engineering cycle critical for optimizing AI agents.

Embed an A2B-style sandbox-as-code directly within workflows to allow isolated, reproducible testing environments as part of the automation pipeline.

Use TypeScript files to define and manage table schemas that render and update in real time on a cloud environment, enabling a code-driven database wo...Show more

Managing database schemas and tables as code with live-edit capabilities simplifies migrations and schema evolution without manual SQL scripts, resemb...Show more