When agentic AI systems invoke external tools, the returned payloads frequently exceed the model's context window token limit — causing silent truncation, hallucinated completions, and non-deterministic agent behavior with no explicit error propagation. This session dissects the root cause of context window overflow in tool-augmented LLM pipelines and presents the Memory Pointer Pattern: an architectural fix that decouples data retrieval from context injection by persisting tool outputs to external object storage or key-value stores and passing typed memory pointers — lightweight reference objects containing URIs, schemas, and metadata — back into the agent's context frame. We'll walk through the implementation covering pointer serialization, lazy dereferencing strategies, token budget allocation algorithms, and graceful degradation when payloads exceed defined thresholds. Attendees will leave with concrete, cloud-agnostic patterns to eliminate silent context overflow failures in production agent systems.