Agent Architecture Review
Trace an AI agent from intent through state, tools, memory and verification, then identify where complexity is actually helping.
Open prompt →Deep technical learning paths, working implementations, and AI prompts you can apply directly to your own code.
Learn tensors, autograd, neural networks, data pipelines, attention, training and a small language model from scratch.
Start learning → 02 Model architectureUnderstand learned components, scorers, policies, hierarchical models and compact reasoning architectures through decomposition.
Start learning → 03 Agent systemsTurn models into systems that plan, use tools, maintain state, search, revise work and verify outcomes.
Start learning → 04 Production AIEngineer autonomous systems for evidence, reliability, authority, distributed execution, security and control.
Start learning →Don’t stop at reading. Use evidence-driven prompts to inspect your own repository, diagnose concrete failures, and apply the mechanisms from the learning paths.
Trace an AI agent from intent through state, tools, memory and verification, then identify where complexity is actually helping.
Open prompt →Apply Models From First Principles Step 01 to a real repository by reconstructing what a scalar quality model is actually learning, what evidence reaches it, and whether its score is meaningful.
Open prompt →Apply Advanced Agents Step 29 to determine what an agent may do autonomously, which actions require escalation, and whether human approval receives enough evidence to be meaningful.
Open prompt →Apply Advanced Agents Step 25 to determine whether a historical agent run can actually be reconstructed, explained and compared after models, prompts, tools and state have changed.
Open prompt →Build a Production AI Agent From First Principles: The Complete Reference Architecture We have spent this series adding mechanisms only when a specific failure demanded them. We …
Read →Treat agent behavior like a production interface: define behavioral contracts, test compatibility across models, prompts, tools, memory, routers and verifiers, then promote changes through explicit release gates.
Read →Learn how to detect silent behavioral regressions in agent systems caused by model, prompt, router, verifier, retrieval, policy and data-distribution drift, then roll back safely using evidence rather than intuition.
Read →Learn how to contain failing model, browser, retrieval, verifier, database and API dependencies with circuit breakers, bulkheads, bounded retries and graceful degradation.
Read →Learn how to schedule many concurrent AI agent runs across shared models, GPUs, browsers, tools and external APIs using admission control, quotas, fairness, priorities, reservations and backpressure.
Read →Can Your Agent Coordinate Across Machines Without Duplicating Work? A single-process agent can already be complicated. It can plan. It can search. It can launch speculative …
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