Agent Systems · core
Agent Communication & Interfaces
Typed messages, events, artifacts, streaming updates, human checkpoints, agent-to-agent protocols, and UI status.
Mental model
Reliable agent interfaces exchange typed state and evidence, not vague prose. Messages need identity, intent, status, result, error, and provenance.
How to study Agent Communication & Interfaces
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use A Survey of AI Agent Protocols, Building Effective AI Agents (Anthropic Engineering), A Survey of Agent Interoperability Protocols: MCP, ACP, A2A, and ANP to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Agent Communication & Interfaces with Multi-agent Coordination, Browser & Computer-use Agents. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Agent Communication & Interfaces and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Agent Communication & Interfaces
Typed messages, events, artifacts, streaming updates, human checkpoints, agent-to-agent protocols, and UI status. Implement designOutline() returning non-empty values for: messageSchema, statusModel, provenance. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with messageSchema, statusModel, provenance plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Why do agent-to-agent messages need typed status and provenance rather than prose?
Build evidence
Synthesize: Agent Systems
Engineer useful agents with bounded loops, tools, memory, protocols, durability, permissions, and long-running control. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks