Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

When deploying autonomous systems to production, token window management and hallucination risks are often the primary barriers to success. While foundational models like Claude 3.5 Sonnet and DeepSeek-V3 offer massive context windows up to 200k tokens, dumping raw logs, database schemas, and conversational history directly into the prompt is an anti-pattern. It leads to attention degradation, skyrocketing latency, and expensive API bills.

To solve this, developers need a systematic approach to context engineering. In this deep dive, we examine a four-layer context engineering framework implemented in Deep Insight, an open-source multi-agent data analysis solution built on the Strands Agents SDK and Amazon Bedrock AgentCore. By leveraging a reliable LLM API aggregator like n1n.ai, developers can dynamically route these layered requests across different frontier models for optimal performance and cost reduction.

Layer 1: Architectural Context Routing

The first layer establishes the structural foundation of your multi-agent system. Instead of treating the context window as a flat text blob, architectural context routing separates concerns by distributing state across specialized agents.

In Deep Insight, the architecture uses a supervisor-worker pattern. The supervisor agent maintains a high-level roadmap and passes only distilled sub-tasks to specialized data analyst workers. This prevents context pollution. When integrating multiple providers, developers often face rate limits and routing complexities. Using n1n.ai allows your infrastructure to handle failover seamlessly, switching between endpoints without altering core agent logic.

from strands_agents import Agent, Supervisor

# Initialize specialized worker with restricted context scope
sql_worker = Agent(
    name="SQL_Analyst