We're Solving Context Engineering for AI Agents at Scale (blog.justcopy.ai)

JustCopy.ai describes a practical, scalable solution to the “context engineering” problem for AI agents: balancing context overload (high token costs, latency, irrelevant info) against context starvation (brittle behavior and edge-case failures). They implemented a three-tier layered architecture—Static Foundation (agent identity and rules), Dynamic Session Context (rolling conversation state with smart windowing and summarization every 5–7 turns), and a Just-In-Time Retrieval layer that injects only task-relevant memory when needed. Retrieval uses embeddings + vector search with rule-based triggers, a 0.75 cosine similarity threshold, and top-k injection (typically k=3–5). Aggressive pruning and periodic compression keep token counts low while preserving semantic continuity. The results are tangible: ~40% reduction in tokens per interaction, 2× better handling of edge cases, 60% faster responses, and fewer hallucinations—showing that dynamic context management improves reliability and efficiency for long-running or autonomous agents. Technical implications for the AI/ML community include designing semantic memory banks, tuning similarity thresholds/top-k, and building tooling to inspect context state. Open challenges remain around optimal compression across domains, recency vs. relevance trade-offs, multimodal context handling, and debugging. Overall, the work reframes context engineering as core infrastructure rather than incremental prompt tuning, with immediate performance and cost benefits for agent-scale deployments.