Agents

Agents in Metaflow

Your autonomous teammate—goal-driven, tool-using, and context-aware.

What is an Agent?

An Agent in Metaflow is a lightweight, goal-oriented executor that makes decisions, runs Flows, uses tools, and adapts based on context. It’s not just a chat interface. It’s an orchestration layer that knows what needs to be done—and figures out how to do it.

You define its:

• Goal: what it’s responsible for

• Tools: what it can use (Flows, APIs, actions)

• Context: what knowledge or constraints it should consider

Once set up, an Agent acts with autonomy. It can reason, plan, execute, reflect, and repeat.

Why Use an Agent?

Use Agents to:

• Offload recurring tasks like lead qualification, social listening, or copy generation

• Handle decisions dynamically, not hardcoded in a workflow

• Run flows with judgment, choosing when and how to trigger steps

• Wrap complex workflows into simple, reusable digital teammates

• Scale your own thinking by giving each Agent a focused role in your GTM stack

Agents allow you to operate at the goal level—not just the task level.

When to Use an Agent

Use an Agent when:

• The outcome is fixed, but the steps may vary

• You want the system to choose what to do next based on data or reasoning

• You’re orchestrating across tools, steps, or clients

• You’re building reusable, goal-driven automation with logic embedded

Don’t use an Agent when:

• You’re testing a prompt or building a one-off Flow

• You don’t need dynamic logic—just a straight path

How Agents Work in Metaflow

Agents in Metaflow are:

• LLM-powered: They use reasoning (ReAct-style or CoT) to plan and execute

• Tool-aware: They know what tools they have access to, including Flows, scrapers, APIs

• Stateful: They can remember, retrieve, and reuse knowledge across runs

• Composable: They can call Flows, reference Records, and chain together operations

An Agent might:

1. Receive a goal like “Summarize our top LinkedIn posts this week”

2. Look up what tools it has (e.g. LinkedIn scraper, summarization Flow)

3. Run those tools in sequence

4. Return a structured summary, auto-saved to a Record

Agents vs Flows

Agents are brains. Flows are muscle.

Key Capabilities

• Set goals and assign tool access

• Chain prompt-based and structured outputs

• Use ReAct-style decision-making

• Handle input/output between Flows and external APIs

• Maintain and retrieve memory from Records

• Can run on schedule, or be triggered from a Flow

How It Fits In

• Agents are autonomous goal-runners

• Flows are callable tools used by Agents

• Canvas is a creative sketchpad agents can populate

• Records store agent output, state, and memory

• Editor lets you craft prompts and schema for Agent input/output