Three terms, three jobs
These short definitions are ours, built on the sources listed at the end.
- Automation follows a fixed recipe: if this happens, do that. It is predictable and cheap, and it breaks when the situation changes.
- An agent is given a goal and chooses its own steps and tools to reach it. It is flexible, but costs more, runs slower and is harder to predict.
- Orchestration is the coordination layer. It decides what runs next, passes context between steps and keeps track of what happened.
What the major sources say
In Building effective agents (December 19, 2024), Anthropic defines workflows as “systems where LLMs and tools are orchestrated through predefined code paths” and agents as “systems where LLMs dynamically direct their own processes and tool usage.”
Microsoft’s Azure Architecture Center guide, AI agent orchestration patterns (February 2026), describes a spectrum from a direct model call, to a single agent with tools, to multi-agent orchestration. It says each level adds coordination overhead, latency and cost, and advises using the lowest level of complexity that reliably meets your requirements.
W.Media reported (June 27, 2025) on a Gartner prediction that over 40% of agentic AI projects could be canceled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls. Gartner also warns of “agent washing”. Predictions are not facts, but the caution is useful.
Orchestration patterns
Microsoft names five orchestration patterns: sequential, concurrent, group chat, handoff and magentic. Anthropic lists five workflow patterns: prompt chaining, routing, parallelization, orchestrator-workers and evaluator-optimizer. They overlap. Prompt chaining resembles a sequential pipeline, and parallelization resembles concurrent work.
For most small and mid-sized businesses the most common shape is the simplest: a process that crosses several tools, such as a lead moving from a CRM to an email sequence to a campaign, where something has to carry updates between them. See our posts on disconnected tools and on what an orchestration layer does.
The plumbing is being standardised
Two open protocols address different needs. Model Context Protocol (MCP) connects an agent to tools and data, while Google’s Agent2Agent (A2A) protocol lets agents connect with each other. SD Times reported on June 23, 2025 that Google was donating A2A to the Linux Foundation. On December 9, 2025, Anthropic announced it was donating MCP to the new Agentic AI Foundation under the Linux Foundation.
Both standards are young. Questions about agent identity, delegated authority and security are still being worked out, which is why we pair this paper with our MCP and agent security checklist.
A simple way to choose
Our own guide, not a finding from the sources. Start at the top and stop at the first level that does the job:
- One model call is enough for many text tasks such as summarising or drafting.
- Plain automation fits simple, repetitive, well-defined tasks.
- A single agent with tools fits tasks where the steps vary but the goal is clear and a wrong action is cheap to undo.
- Orchestration fits processes that cross several tools or need several steps, agents or people to hand work to each other.
- Multi-agent designs are worth their extra cost only when a simpler design has been tried and does not work.
Whatever you choose, keep an owner for the outcome and a person approving anything costly. See our whitepaper on AI agents for business.
Sources
- Anthropic, Building effective agents, December 19, 2024
- Microsoft Learn, AI agent orchestration patterns, February 2026
- Anthropic, Donating the Model Context Protocol and establishing the Agentic AI Foundation, December 9, 2025
- SD Times, Google’s Agent2Agent protocol finds new home at the Linux Foundation, June 23, 2025
- W.Media, Over 40 percent of Agentic AI projects could face the axe by end of 2027: Gartner, June 27, 2025
Frequently asked questions
What is the difference between automation and an AI agent?
Automation follows steps that people defined in advance. An agent is given a goal and chooses its own steps and tools. Anthropic describes workflows as LLMs and tools orchestrated through predefined code paths, and agents as LLMs that dynamically direct their own processes.
What is AI orchestration?
It is the coordination layer that decides which step, tool or agent runs next, passes context between them and tracks what happened. It can coordinate fixed workflows, agents, or both.
Do I need AI agents?
Not always. Microsoft’s guidance says to use the lowest level of complexity that reliably meets your requirements, and simple repeatable tasks are usually better served by plain automation or a single model call.
What is agent washing?
Gartner uses the term for rebranding existing assistants, chatbots or RPA as agents. Ask a vendor which steps the system decides on its own and which are fixed.
What you get:
- Plain definitions of automation, agents and orchestration
- What Anthropic, Microsoft and Gartner say, with sources
- The main orchestration patterns and how they overlap
- Where MCP and A2A fit
- A least-complexity decision guide you can apply today