The journal / AI Automation Engineering
The engineering notebook.
Practical engineering for useful AI systems. Technical tutorials, working examples, and considered approaches to automation.
7 articles
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Build Retrieval That Can Explain Its Sources
Design retrieval around authorized evidence, stable document revisions, useful context, and claim-level provenance instead of a larger context window.
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Choose a Workflow Before You Choose an Agent
Match autonomy to the actual uncertainty in a task, with a practical decision process for workflows, bounded agents, and human escalation.
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Design Tool Contracts an Agent Can Use Correctly
Make tools discoverable, narrow, and recoverable with explicit schemas, authorization boundaries, useful results, and honest failure semantics.
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Deterministic Patterns for Agentic Applications: A Practical Guide
Build reliable agent systems with validated proposals, pure state transitions, durable observations, scoped approvals, and transactional execution intents.
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Make Retries Safe Before You Automate More
Design operation identities, retry budgets, and reconciliation paths that prevent an interrupted request from becoming duplicate work.
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Observe Agent Runs and Enforce Useful Budgets
Trace the complete task, separate waiting from work, and enforce cost and latency limits without losing track of uncertain side effects.
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Put Approval at the Point of Commitment
Bind human approval to a concrete action, authenticated identity, and resource version so execution still matches what was reviewed.
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