Frequently Asked Questions
Common Questions About Building Context-Aware AI Agents
Everything you need to know before registering.
What makes an AI agent genuinely context-aware? +
A genuinely context-aware AI agent has four forms of awareness: task awareness (a semantic blueprint defining its current goal and constraints), knowledge awareness (access to relevant knowledge through RAG plus explicit knowledge boundaries), system awareness (its role within the multi-agent orchestration via MCP), and state awareness (the current conversation and task state). Building all four into every agent is what this workshop teaches.
How do semantic blueprints give AI agents context awareness? +
Semantic blueprints are structured specifications that give each agent explicit context about its role, goal, relevant knowledge, constraints, output format, and available tools. Rather than relying on the LLM to infer context from a raw prompt, a semantic blueprint makes context explicit and structured, giving the agent genuine awareness of its situation rather than implicit understanding.
How does a context-aware agent handle situations outside its knowledge boundary? +
A well-designed context-aware agent knows what it does not know. The semantic blueprint includes explicit knowledge boundary definitions: topics the agent should not answer confidently. When a query falls outside those boundaries, the agent either escalates to a more specialised agent via MCP or flags the response as uncertain rather than hallucinating confidently.
How is context awareness different from having a large context window? +
A large context window gives an agent more text to work with. Context awareness is the architectural discipline of structuring what goes in that context window. An agent with a small but well-structured context (semantic blueprint, relevant RAG retrievals, explicit role definition) outperforms an agent with a large but unstructured context. Context engineering is about quality and structure, not just quantity.
How do I test whether my AI agent is truly context-aware? +
Context awareness testing involves checking that the agent: correctly identifies its task from the semantic blueprint, stays within its defined knowledge boundaries, correctly escalates out-of-scope requests, maintains consistent state understanding across a multi-turn conversation, and produces outputs that match its blueprint output format specification. The workshop covers a context awareness test suite for multi-agent systems.
Can I retrofit context awareness into an existing AI agent? +
Yes. The most impactful change is adding semantic blueprint generation to an existing agent. Even a simple blueprint that defines the agent's role, knowledge domain, and output format significantly improves reliability. The workshop covers incremental context awareness improvements that can be applied to existing agents without full rebuilds.
