Advanced Agent Systems
Move from one bounded harness to cooperating systems. Learn context, memory, tool boundaries, MCP, A2A and delegation without mistaking a protocol name for an architecture.
A harness becomes an agent
An agent is not a chat window with tools. It is a bounded control loop that can observe, propose, act and stop under rules that somebody can inspect.
Context is a budget, not a brain
A model can only work from the information supplied in this turn. Good context is selected evidence with a job to do, not every document you can fit into a window.
Memory: working, durable and retrieved
Memory is not one magical feature. Separate what is needed now, what the system must retain as a record, and what it may retrieve later.
Tool calls are proposals
A tool call from a model is structured input to your application. Validate it, authorize it and record it before it changes anything.
MCP without the fog
MCP is a way for an AI application to discover and use declared capabilities. It does not replace your authorization, your API design or your production controls.
A2A: handing work to another system
A2A is about one agentic system asking another to perform a bounded task. It is different from giving a host direct access to a tool.
Multi-agent systems: roles, delegation and evidence
Specialized roles can help when they create a useful boundary. They create theatre when they merely split one prompt into several names.
Gateways, policy and an understandable system
A gateway can make cross-cutting controls consistent, but it cannot decide your architecture for you. Put identity, policy, limits and evidence at boundaries you can operate.