Checkpointing
Checkpoint every meaningful transition: inputs, tool calls, decisions, outputs, and recovery metadata.
Durable AI Execution
Patterns for AI workflows that survive partial failure and remain inspectable after each run.
memory architecture diagram. Nodes: event, encode, episodic, semantic, recall, compress. Flow: event to encode (primary); encode to semantic (primary); semantic to recall (primary); encode to episodic (supporting); episodic to recall (supporting); recall to compress (feedback).
Replay
Replay turns failures into inspectable events instead of ambiguous model behavior.
Memory Layer
Episodic memory, semantic retrieval, compression, and replayable context for agents that need continuity.
memory architecture diagram. Nodes: event, encode, episodic, semantic, recall, compress. Flow: event to encode (primary); encode to semantic (primary); semantic to recall (primary); encode to episodic (supporting); episodic to recall (supporting); recall to compress (feedback).
Workflow Engine
Durable execution, retries, branching, checkpoints, and human gates for AI workflows in production.
workflow architecture diagram. Nodes: start, branch, retry, gate, commit, checkpoint. Flow: start to branch (primary); branch to gate (primary); gate to commit (primary); branch to retry (supporting); retry to branch (loop, correction path); gate to checkpoint (feedback).
Further reading
Related projects
Projects where I apply this concept. The projects page shows the code and maturity of each one.
Have a project in mind?
Write to info@gianlucamazza.it with the operational problem and the systems involved. When a working prototype already exists, the first step is the production readiness review. That review does not include code changes.