Beyond Prompting: Building Production-Grade Systems with Context and Loop Engineering
About this session
Moving enterprise GenAI from proof-of-concept sandboxes into resilient production reveals a stark reality: relying on prompt engineering to enforce complex workflows is a liability. When autonomous agents tackle long-running enterprise tasks, systems inevitably degrade due to token bloat, brittle tool execution, and unmanaged state degradation. This presentation delivers a battle-tested architectural framework for decoupling core model execution from system engineering boundaries. We break down the technical patterns behind Context Engineering (the data supply chain for active token pruning) and Loop Engineering (programmatic state machines for self-correcting validation). Attendees will walk away with framework-agnostic blueprints to shift reliability away from soft prompt formatting and onto deterministic system constraints.
Speaker
Key takeaways
- Production-Grade Context Management: Mastering algorithmic context pruning and state synchronization to prevent token bloat in long-running agent workflows.
- Deterministic State Workflows: Blueprints for engineering iterative loops with explicit automated validation and convergence guarantees.
- Open System Architectural Patterns: Repeatable, vendor-agnostic infrastructure strategies to successfully move GenAI from sandboxes to production.
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