- Explore & research: track the agentic AI landscape, evaluate new models, tools, and ideas, and run quick experiments to prove what is worth pursuing.
- Develop AI agents: design and ship agents that reason, plan, use tools/APIs, and handle memory and retrieval (RAG).
- Orchestrate multi-agent systems: build systems where specialized agents collaborate, delegate, and hand off tasks to solve complex problems.
- Build with agent frameworks: implement agents using frameworks such as Strands, LangGraph, CrewAI, or AutoGen, applying the right patterns for each use case.
- Implement MCP & integrations: connect agents to tools, data, and services through the Model Context Protocol (MCP) and other AI integration technologies and APIs.
- Deploy on the cloud: package, deploy, and run agents on cloud platforms (AWS, GCP, or Azure) with scalability, security, and cost in mind.
- Add observability: instrument agents with logging, tracing, and monitoring so behavior can be measured, debugged, and improved in production.
- Productionize & evaluate: define quality, success, latency, and cost metrics, build eval harnesses, and optimize for reliability and scale.
- Ensure safety: apply guardrails, tool permissions, and human-in-the-loop checks for responsible agent behavior.
- Collaborate & enable : partner with product and domain teams, document decisions, and raise the team's overall agentic-AI fluency.
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