An agentic workflow lets a model choose the next step at runtime instead of following a predefined path, distinguishing it from scripted automation per Anthropic's workflow-versus-agent taxonomy. Six components are required for production: a decision-making model, tool interfaces, state and memory, an execution environment, observability, and a policy/permission layer (the one teams underbuild). The piece walks through the execution loop, compares agentic workflows against deterministic pipelines on six properties (notably blast radius bounded by permissions rather than code), lists use cases where the pattern earns its cost (repository triage, support escalation, data-quality investigation), and gives a build sequence emphasizing goal checks, read-only dry runs, gated writes, stopping conditions, scoped identity, and tracing. It closes by framing cloud permission scope as a security question that platforms like Orca address via asset, identity, and data context mapping.
Table of contents
What are agentic workflows?Components of AI agentic workflowsHow agentic workflows workUse cases and examples for agentic workflowsAdvantages of agentic workflowsBuilding agentic workflows in practiceSecure Agentic Workflows in the Cloud With OrcaFrequently Asked Questions About Agentic WorkflowsQuestions this post answers
What is the difference between a workflow and an agent according to Anthropic's engineering guidance?
Workflows are systems where LLMs and tools are orchestrated through predefined code paths written by a developer, while agents dynamically direct their own process and tool usage at run time. Anthropic published this distinction in its Building Effective Agents guidance on December 19, 2024, along with five composable patterns: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer. Developers deciding between scripted automation and agent-driven systems can track these framework distinctions on daily.dev.
What are the six components a production agentic workflow needs?
A working agentic workflow needs a model that decides the next step, a tool interface with named schemas, state and memory for sessions and long-term facts, an isolated execution environment for tool calls, observability with per-step tracing, and a policy and permission layer controlling which tools can be called with which credentials. The policy layer is the component teams most often build last, even though enforcing rules like 'never delete production data' at the tool boundary is what actually makes them a control rather than a request. Teams scoping their first production agent can compare component checklists like this one on daily.dev.
Who is accountable when an AI agent takes a wrong action with cloud credentials?
The team that granted the credentials is accountable, because the blast radius of a wrong step in an agentic workflow is bounded by the permissions granted rather than by code paths a developer wrote and reviewed. Framing accountability around the permission set rather than machine responsibility points at a fixable artifact: if a role could do something uncomfortable, the permission set is the defect. Engineers scoping agent identities and credentials follow governance debates like this on daily.dev.