Agents and automation

Agentic AI

Agentic AI refers to AI systems that can take autonomous actions to accomplish goals — browsing the web, executing code, managing files, and interacting with other services. Unlike chatbots that only respond to prompts, agentic AI can plan, execute, and iterate on multi-step tasks independently.

How it differs from traditional AI

Traditional AI tools (including most chatbots) follow a simple pattern: you provide input, they produce output. The interaction ends there.

Agentic AI breaks this pattern. It can:

• Plan multi-step workflows. Given a high-level goal, it breaks it down into steps and executes them in sequence.
• Take actions in the real world. Browse websites, write and run code, send messages, manage files — not just generate text.
• Iterate and self-correct. When something goes wrong, it can diagnose the issue and try a different approach.
• Maintain context across tasks. It remembers what it's done and uses that context to inform next steps.

Real-world applications

Agentic AI is already transforming how work gets done:

Software development. Tools like Claude Code don't just write code snippets — they plan architectures, implement features across multiple files, run tests, and debug issues autonomously.

Research and analysis. Agentic systems can search multiple sources, synthesize findings, and produce comprehensive reports without constant human direction.

Business process automation. From processing invoices to managing customer onboarding flows, agentic AI can handle complex workflows that previously required human oversight at every step.

Why organizations should pay attention

Agentic AI represents the next major leap in AI capability. Organizations that understand and adopt agentic workflows early will have a significant advantage — not just in productivity, but in the kinds of problems they can solve.

MCP (Model Context Protocol)

The Model Context Protocol is an open standard developed by Anthropic that defines how AI models connect to external tools, data sources, and services. Think of it as a universal connector — like USB for AI. Before MCP, every AI integration required custom development.

With MCP, any tool that supports the protocol can connect to any AI model that supports it. For businesses, MCP is what makes it possible to connect Claude to your CRM, your documents, your databases, and your internal tools — without custom engineering for each integration.

AI Agent

An AI agent is an AI system that can take actions autonomously to complete a goal — not just generate text, but actually do things. Browsing websites, writing and executing code, managing files, sending messages, filling forms, calling APIs. An agent operates in a loop: it receives a task, makes a plan, takes an action, observes the result, and decides what to do next — repeating until the task is complete or it needs human input.

AI agents are the technology underlying tools like Claude Code and Claude Cowork. They represent a significant capability jump from conversational AI because they can complete entire workflows, not just respond to individual prompts.

AI Workflow

An AI workflow is a sequence of steps in which AI tools handle one or more stages of a process — either automatically or with human review at defined checkpoints. For example: a document arrives by email → AI extracts key information → AI categorizes and routes it → human reviews the categorization → AI drafts the response → human approves and sends. Building AI workflows is how organizations move from AI as an occasional tool to AI as an embedded part of how work gets done.

The most impactful AI workflows are those that target high-volume, systematic processes where human time is currently consumed by tasks that follow predictable patterns.

Automation

Automation is the use of technology to perform tasks with minimal human intervention. In the context of AI, automation refers specifically to using AI to handle tasks that previously required human judgment, language understanding, or content generation — not just rule-based tasks that traditional software could handle. AI-powered automation is qualitatively different from earlier automation: it can handle variation, ambiguity, and unstructured inputs (like natural language) that rule-based systems cannot.

For businesses, AI automation is most valuable for high-volume tasks with clear outputs: email triage, document processing, report generation, data extraction.

Multi-Agent System

A multi-agent system is an AI architecture in which multiple AI agents work together — each handling a specific part of a complex task — rather than a single agent doing everything. One agent might handle research, another analysis, another writing, another review. Multi-agent systems are more powerful than single agents for complex, multi-step tasks because they can parallelize work, apply specialized capabilities to each stage, and check each other's outputs.

They also introduce new challenges: coordination, context transfer between agents, and handling conflicts when agents reach different conclusions.

Orchestration

Orchestration in AI refers to the management and coordination of multiple AI agents, models, or tools to complete a complex workflow. An orchestrator decides which agents to use, in what order, how to pass information between them, and how to handle errors or unexpected results. Orchestration is to multi-agent AI what project management is to a human team — it ensures the parts work together toward a coherent outcome.

As AI workflows become more complex, orchestration becomes increasingly important for reliability and quality.

AI Pipeline

An AI pipeline is a sequence of processing steps — some involving AI, some not — that takes an input and produces a defined output. For example: raw customer feedback → AI categorization → AI sentiment analysis → human review → formatted report. Pipelines are how organizations build reliable, repeatable AI workflows rather than ad hoc AI use.

A well-designed AI pipeline is testable, auditable, and improvable — you can measure quality at each stage and make targeted improvements without rebuilding the whole thing.

AI Sandbox

An AI sandbox is an isolated environment where AI tools can be tested and experimented with without affecting production systems or real data. Sandboxes allow developers and users to try new AI capabilities, test prompts, and evaluate outputs safely — catching errors before they affect real workflows. For organizations building AI tools, sandboxes are essential for quality assurance.

For individual users, "sandboxing" a new AI workflow before using it on important work is good practice: test it on low-stakes examples first, then scale up when you're confident in the output quality.

Autonomous Agent

An autonomous agent is an AI system that can complete complex tasks with minimal human intervention — setting its own sub-goals, making decisions at each step, using tools, and adapting when something doesn't work. Unlike AI tools that respond to single prompts, autonomous agents can run for extended periods, executing dozens or hundreds of steps independently. Current autonomous agents are impressive but imperfect: they work well for well-defined tasks with clear success criteria and struggle with open-ended tasks, novel situations, or processes that require significant judgment.

The degree of autonomy to grant an agent should be calibrated to the consequences of errors.

Task Decomposition

Task decomposition is the process of breaking a complex task into smaller, more manageable sub-tasks — a technique that significantly improves AI performance on multi-step problems. When you ask an AI to do something complex in a single prompt, it often produces lower quality output than if you break the task into stages. "Research these five companies, then compare them on these criteria, then recommend the best option" produces better results as three sequential prompts than one.

AI agents use task decomposition automatically; human users can improve their results by decomposing tasks manually before prompting.

Tool Use (AI)

Tool use refers to an AI model's ability to call external tools — search engines, calculators, code interpreters, APIs, databases — to assist with tasks rather than relying solely on its internal knowledge. Tool use dramatically extends what AI can do: a model without tool use can only answer based on its training data; a model with tool use can search the web for current information, run code to do calculations, or query a database for specific records. Most capable AI agents have tool use — it's what allows them to take actions in the world rather than just generating text.

Memory (AI)

Memory in AI refers to the ability of an AI system to retain and recall information across interactions — not just within a single conversation, but over time. Basic AI tools have no persistent memory: each conversation starts fresh. Memory-enabled systems can recall previous interactions, user preferences, and accumulated context.

For AI agents handling ongoing workflows — managing a project, supporting a recurring business process, building understanding of a client — memory is essential for quality and efficiency. Different AI systems implement memory differently: some store it in structured databases, others in summarized form, others by including full conversation history in each request.