What makes 5% of AI agents work in production?
4 days ago
- #Context Engineering
- #AI Agents
- #Memory Design
- 95% of AI agent deployments fail in production due to inadequate scaffolding around models, such as context engineering, security, and memory design.
- Context engineering is crucial for AI systems, involving feature selection, validation, and observability to ensure relevant and structured knowledge retrieval.
- Advanced context engineering includes semantic and metadata layering to normalize across input formats and ensure relevance beyond similarity.
- Text-to-SQL implementations are challenging due to natural language ambiguity and domain-specific business terminology, requiring extensive context engineering.
- Governance and trust are critical, with lineage, permissioning, and policy gating necessary to prevent organizational and compliance issues.
- Human-in-the-loop design is a common trait among the successful 5% of AI agents, positioning AI as an assistant rather than an autonomous decision-maker.
- Memory in AI systems is architectural, involving user, team, and organizational levels, and requires careful design to balance UX, privacy, and system implications.
- Multi-model inference and orchestration patterns are emerging, with routing logic based on task complexity, latency, cost, and regulatory concerns.
- Natural language interfaces are best suited for complex, exploratory tasks, while GUIs are preferable for straightforward, iterative tasks.
- Underexplored areas include context observability, composable memory, domain-aware DSLs, and latency-aware UX, presenting opportunities for innovation.