Build a Deep Research Agent with Systems thinking Approach

Start from a single LLM call, add tool calling, then wrap it in the loop that turns a model into an agent

See exactly how a model requests a tool and how you dispatch it, so nothing is a black box

Add state and a stopping policy so the agent knows when it is actually done

Design the system prompt, tool schemas, memory, and context that steer agent behavior

Add a planner, guardrails, and context compaction so long runs do not fall apart

Keep the agent grounded and predictable across complex, multi-turn interactions

Capture step-by-step traces that show exactly where and why a run went wrong

Use structured, cited outputs so every claim maps back to a real source

Turn opaque agent failures into inspectable, fixable events

Build an eval set and score runs with a rubric plus an LLM-as-judge

Track grounding, faithfulness, citation quality, and hallucinations across runs

Instrument token and dollar cost so reliability does not mean runaway bills

Write tool schemas and results that teach the model, not just return raw data

Handle failures, empty results, and errors so the agent recovers instead of crashing

Build predictable behavior into complex, multi-step tool use

Move from a hand-built loop to the Claude Agent SDK and see what it handles for you

Understand precisely what the SDK gives you versus what you still have to design

Connect tools and external context through MCP into a reusable architecture