What are Agentic Workflows?
Agentic workflows replace linear automation with autonomous AI agents that can adapt, research, and execute complex series of B2B marketing tasks without continuous human oversight.
Moving Beyond Simple Automation
Agentic workflows describe a method of automation where software relies on Artificial Intelligence (specifically Large Language Models, or LLMs) acting as autonomous “agents” to execute tasks that traditionally required human cognition, reasoning, or judgment.
Unlike rigid programmatic workflows (like a Zapier integration that fires only when an exact trigger occurs), agentic workflows operate on goals and context.
The Shift from ‘If/Then’ to ‘Achieve This’
Unlike rules-based systems that break when the unexpected happens, agents adapt. A few examples of what that looks like in practice:
- Handle Uncertainty: An agent can research a company, encounter an unexpected website structure, process the new layout, and still successfully extract the required pricing data.
- Execute Multi-step Reasoning: Agents can take a high-level goal (e.g., “Identify the top 3 rising topics in cybersecurity on LinkedIn this week”) and independently break it down into research, summarization, and reporting steps.
- Self-Correct: If an API call fails or a data source is ambiguous, a well-designed agent retries, pivots, or flags the issue rather than silently failing. This requires deep contextual reasoning — the agent operates with a goal, and determines the necessary steps sequence.
For Go-To-Market teams, this means automating the “boring but necessary” research and synthesis tasks: turning raw social data, product features, and community threads into high-value assets and insights overnight.