OpenAI API Docs
Agent orchestration and handoffs guide
Provides the official view that agent design is about orchestration, handoffs, and workflow structure rather than raw prompting alone.
Open sourceBack to roadmapView standards
Systems
PremiumStop Treating Agents Like Magic: Tool Use and Workflow Design
A reliable agent does not act because the model sounds confident. It routes through clarification, evidence, action boundaries, recovery order, and reusable operator skills.
30 min
Intermediate
Trust Layer
Why this lesson is worth learning
This lesson is not assembled from random fragments. It is organized as official definition + product abstraction + executable practice.
Learning Objectives
Separate model reasoning from tool execution, confirmation, and recovery logic
Design one workflow with clarify, retrieve, decide, act, verify, and handoff stages
Turn repeated operator behavior into a reusable SOP or skill instead of one-off clicking
Practice Task
Pick one workflow that uses tools, APIs, or external systems. Define its trigger, preconditions, required evidence, confirmation rule, fallback path, stop condition, and the one part that should become a reusable skill or SOP.
Editorial Review
Reviewed · DepthPilot Editorial · 2026-03-09
The lesson is grounded in current OpenAI and Anthropic guidance on orchestration, handoffs, and tool design.
It also uses a credible practitioner source on middleware to keep the teaching anchored in real workflow control patterns.
The framing deliberately treats agent behavior as a systems problem so learners build judgment instead of cargo-culting autonomy.
Primary Sources
OpenAI API Docs
Function calling
Anchors the lesson's distinction between structured outputs and real tool actions that affect the outside world.
Open sourceAnthropic Engineering
Building effective agents
Supports the lesson's emphasis on decomposition, routing, and choosing stable workflow patterns over agent mystique.
Open sourceAnthropic Engineering
Writing effective tools for agents — with agents
Reinforces the idea that tool quality, interface design, and execution boundaries shape agent reliability.
Open sourceLangChain Blog
Agent middleware
Adds a practitioner view on inserting checks, routing, and policy logic between model decisions and tool execution.
Open sourceKnowledge chain
This lesson is not a standalone article. It is one node inside the larger network. Read it as part of a chain, not as isolated content.
Open the full knowledge networkProof you actually learned it
You can map one workflow into clarify, retrieve, decide, act, verify, and handoff stages instead of calling it 'the agent flow'.
You can explain which action requires confirmation, which tool input requires evidence, and where the chain should stop or degrade.
Most common traps
Calling a workflow agentic when it is really just a prompt glued to a risky tool action.
Adding more tools before defining routing order, action boundaries, or recovery logic.
01
Tool use is workflow design, not agent magic
The important question is not whether the model can call a tool. The important question is whether the workflow knows when the model should clarify first, retrieve evidence first, wait for confirmation, or escalate to a human. Once actions touch real systems, tool use becomes orchestration design.
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