Learning Objectives & Key Takeaways
0 / 4 Completed- Understand AI Trends core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
- Deterministic tool ordering in MCP keeps prompt caches warm and protects agent latency and cost.
- MCP servers expose tools, resources, and prompts, each discovered through its own list method.
- A tools/call result can include structuredContent that must match a declared outputSchema.
- Reasoning models, small models, multimodality, and evals are fast-moving trends, not fixed specs.
- Every MCP flow needs a lifecycle handshake before tools/list, resources, or prompts/list calls.
Production Agents and the Cache That Stopped Saving Money
Most public conversation about AI agents this year focuses on what the model decides, but the incidents that actually page engineers at night are almost always plumbing problems. A team running an internal expense-filing agent discovered this the hard way when a routine, seemingly unrelated deploy quietly doubled their per-request latency and token bill without changing a single prompt or model.
The agent's tool server built its list of available tools by scanning a plugins directory at startup, and the filesystem did not guarantee a stable scan order across restarts. Every time the service redeployed, the block of tool definitions returned in the tools/list response came back in a slightly different order, even though the tools themselves had not changed at all.
- Reasoning Models: Buying Time to Think
- Model Context Protocol: Teaching Agents a Common Grammar
- Small and On-Device Models: Compute Moves Closer to the User
- Multimodality Beyond the Demo Reel
- Evaluation: The Discipline Interviewers Now Actually Ask About
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