AI Agent Development: End of Year Reflections
Patterns that have become obvious in AI agent development - from code-first approaches to continual learning and the convergence of RL, multi-agent systems, and context management.
Read note →William Hitchcock
Applied AI researcher and engineer.
I build AI systems that hold up in the messy real world—mixing research rigor, pragmatic engineering, and thoughtful evaluation to deliver when it counts.
About
I’m an applied AI researcher and engineer with a background spanning knowledge graphs, agentic AI systems, forecasting, NLP, and post-training techniques like SFT and reinforcement learning. I’ve spent my career building systems that understand, anticipate, and respond to real-world events.
I started my career at Acertas Analytics, developing global-scale knowledge graphs for event forecasting and early-warning analysis—blending unstructured text, structured intelligence, and temporal signals into models that support strategic decision-making.
At Booz Allen, I led agentic AI R&D for mission transformation, focusing on deep agents and just-in-time multi-agent system construction, and scalable architectures for goal-directed LLM behavior. I also served as lead scientist for Propster, designing retrieval pipelines, content generation systems, and evaluation frameworks.
My current work centers on building advanced ML and NLP solutions to detect and analyze community-impacting events, integrating causal modeling, knowledge graphs, and temporal forecasting with LLMs to plan and respond in a goal-aligned, causally grounded way.
Outside of work, I’m exploring how reinforcement learning, reasoning models, and test-time compute strategies can be combined to improve decision-making and planning in large language models.
Currently
- Building ML and NLP pipelines to detect and analyze community-impacting events, integrating causal modeling, knowledge graphs, and temporal forecasting with LLMs to craft plans and responses that stay aligned to goals and causal context.
- Researching reinforcement learning and test-time compute strategies to strengthen LLM reasoning and planning.
- Benchmarking memory systems and evaluating domain-specific AI agent deployments to ensure reliable behavior in context-heavy tasks.
Let’s connect
Want to collaborate?
I’m always excited to chat about new ideas, pair on a tricky bug, or join forces on a community event. Drop a note and I’ll reply soon.
Email willhitch9@gmail.com
Location Based in Arlington, VA — collaborating remotely everywhere.
Writing
Blog highlights
Essays on applied AI, agentic systems, and the experiments that push my research forward.