About Exalted AI Team
Joe Chang
Learn AI insights
Build thinking machines
A capable model becomes a capable AI system through the engineering around the model. Exalted AI connects Prompt Engineering, Context Engineering, Harness Engineering, Loop Engineering, and Graph Engineering into a larger architecture for agentic systems. Instructions shape behavior. Context supplies working knowledge. Harnesses provide tools, state, execution, and control. Loops turn outcomes into feedback. Graphs coordinate larger systems of agents and dependencies. The goal is to develop the architectural judgment to design AI systems that can act, recover, adapt, and improve across real engineering workflows.
Experience behind the lessons
Exalted AI Team
Joe Chang
Joe Chang brings more than 20 years of software engineering experience across startups, research, international banking and FinTech, entrepreneurship, and production systems. He has taught more than 1,000 students across machine learning, deep learning, reinforcement learning, large language models, and AI engineering. Exalted AI extends that teaching into an advanced technical reference for building modern agentic systems.
- Design the right control architecture. Choose among deterministic pipelines, state machines, model-directed workflows, and autonomous agents according to task structure, uncertainty, risk, and required control.
- Turn course concepts into engineering judgment. Deepen each lesson with insightful explanations, clear diagrams, realistic failure cases, and self-assessment questions that test whether the architecture truly makes sense.
- Stay close to the cutting edge of agentic AI. Learn the mechanisms behind important new ideas so you can recognize which advances genuinely change how AI systems are engineered.
- Position yourself for high-paying AI engineering work. Build advanced skills in agent architecture, context, harnesses, feedback loops, evaluation, and modern AI systems that increasingly matter in high-value technical roles.
- Bring enterprise systems into the LLM era. Connect existing applications, data, APIs, workflows, permissions, and governance with modern context, tool, agent, and feedback architectures.
- Recognize what matters beneath the AI hype. Judge new ideas by the mechanism they change, the capability they unlock, and their effect on reliability, cost, leverage, and system architecture.
- Engineer prompts as behavioral interfaces. Turn objectives, inputs, examples, constraints, outputs, and acceptance criteria into explicit contracts for model behavior.
- Engineer memory and persistent state. Separate working context, semantic memory, episodic memory, procedural knowledge, checkpoints, threads, stores, and durable artifacts.
- Build the harness that turns a model into an agent. Add tools, skills, state, persistence, execution environments, authorization, recovery, and observability around the model.
- Build systems that recover from failure. Design retries, checkpoints, idempotent operations, exception boundaries, durable execution, and recovery paths for long-running agent work.
- Design loops that improve the work. Connect action, observation, evaluation, critique, revision, verification, and retry into bounded feedback systems.
- Coordinate intelligence across graphs. Model agents, dependencies, shared state, communication, routing, and control as larger graph-structured AI systems.