AI agent development - Peter Jonathan Wilcheck
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How AI Agents Learn to Reason

The trustworthiness of AI agents deployed in production depends on the rigor of their training methodologies. Traditionally, reinforcement learning from human feedback (RLHF) has been used, where human evaluators rank model responses to guide preferred behaviors. However, human preference is slow, costly, subjective, and does not guarantee correctness. Reinforcement learning from verifiable rewards (RLVR) offers.. Read more
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Stateful Execution and Checkpointing for Reliable Long-Running AI Agents

A single prompt-and-response call is easy to trust. The moment an AI agent strings together a dozen steps — researching, calling APIs, writing to a database, waiting on a human approval — reliability becomes the whole job. If the process crashes on step nine, you cannot simply start over: side effects have already fired, tokens..

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Building Production-Ready AI Agents with LangGraph and CrewAI Frameworks

Moving an AI agent from a weekend prototype to something a business can depend on is where most projects stall. A single prompt loop that calls a few tools is easy to demo and hard to trust. Production work demands reliability, observability, and the ability to recover when a step fails halfway through. Two open-source..

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Multi-Agent Systems: How Coordinated AI Agents Tackle Complex Workflows

Multi-agent systems coordinate specialized AI agents that plan, divide labor, and work in parallel to handle complex workflows a single agent cannot. A practical look at architecture, frameworks, and reliability.

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