An agentic workflow is a process where AI agents make some of the decisions about what to do next — instead of just executing fixed steps you laid out in advance.
An agentic workflow is a process where the order of operations isn't fixed in advance — an AI agent decides what to do next based on what it has just learned. Compare it to a Zapier-style workflow, where you pre-define every step ("when X happens, do Y, then do Z"). In an agentic workflow, the agent might do Y, see something unexpected, and do W instead.
Three things characterise an agentic workflow. First, branching is dynamic — the agent picks the path. Second, the agent uses tools to act on what it decided. Third, there's a loop — the agent observes the result, adjusts, and continues until the goal is met or a guardrail stops it.
Most useful business processes today are partially agentic and partially deterministic. The deterministic parts are the rules that should never bend ("never auto-send to a customer without human approval"). The agentic parts are the judgement calls that used to require a human ("is this lead worth a call this week?"). Drawing that line well is the hard part.
Inbound-lead handling. A traditional workflow would route every lead to the same form-fill, score it the same way, send the same email. An agentic workflow lets the agent decide: this lead mentioned a specific competitor we beat on price — send a different email. This lead mentioned a deal-breaker feature we don't have — flag for human review and don't book the call. This lead is asking a basic FAQ — answer directly and follow up in three days.
Most real business processes don't follow neat rules. Customer enquiries vary, edge cases are common, and the cost of treating every case the same is lost revenue and frustrated humans. Agentic workflows handle the variation that traditional workflow tools choke on.
The trade-off is predictability. A deterministic workflow does the same thing every time, which is great for compliance and easy to debug. An agentic workflow adapts, which is great for outcomes but harder to predict and audit. The right balance is rarely all-agentic — it's a deterministic shell with agentic decision points where they add value.
For non-technical founders, the practical implication is that you can finally automate the parts of your work you couldn't before — the parts that needed judgement. The hard work is defining where the agent has discretion and where it doesn't.
Squidgy lets you draw those boundaries explicitly. When you build an agent, you specify which decisions the agent makes on its own and which need human approval before going out. You also specify escalation paths — when the agent should stop and ask a human, instead of guessing.
Most builders start with a heavy approval layer (every customer-facing message gets a human eye) and dial it back as the agent proves itself. The platform makes that progression easy to manage.
Deterministic workflows execute the same steps in the same order every time. Agentic workflows let an AI decide what to do next based on what it just saw. Deterministic is predictable but brittle; agentic is adaptive but harder to predict.
Use deterministic when the steps are stable and the consequence of getting it wrong is high (compliance, payments, regulated communications). Use agentic when the input varies a lot and human judgement was the bottleneck (qualification, triage, drafting). Most real workflows mix both.
Approval queues with batching: the agent does its work, queues anything customer-facing for a human review, and a human approves a batch in one short session per day. Queueing keeps the agent fast; batching keeps the human productive.
Usually the model misreads context and picks a worse path, or it tries to use a tool in a way that doesn't fit the situation. Eval and approval queues catch most of this. Without them, the failures are silent and you find out from a customer.
Glossary
What is AI agent?
An AI agent is software that takes a goal, decides what steps to take, uses tools to do them, and carries the work out with little or no human prodding between steps.
Glossary
What is Agent orchestration?
Agent orchestration is the layer that decides which agent (or which step) handles which part of a task — coordinating multiple agents, tools, and humans toward an outcome.
Glossary
What is Multi-agent system?
A multi-agent system uses several specialised AI agents that each handle part of a task, coordinated by an orchestrator — like a team rather than a soloist.
Glossary
What is Agent builder?
An agent builder is a tool for creating AI agents — defining what they do, what tools they can use, and how they decide — without writing all the code yourself.
No code. Hands-on onboarding from the team in your first cohort.