AI agents sound most exciting when they look independent.
- They read the request.
- They pick the tool.
- They decide the next step.
- They write the response.
- They trigger the action.
That feels powerful.
It is also where things can get uncomfortable. Because a product workflow is not always something the model should invent while it is running.
- A refund has rules.
- A billing change has checks.
- A CRM update needs the right account.
- A support ticket needs context before it is closed.
- An account action needs permission before anything changes.
This is why Google ADK 2.0 Workflows is worth paying attention to.
The update is not just another AI agent feature. It points to a better way of thinking about production AI: Let the product control the path. Let AI help where judgment is needed.
What Google ADK 2.0 Workflows is about
Google ADK 2.0 adds workflow support for building agents with more structure.
Instead of letting the model decide every step on its own, developers can define the route more clearly.
That route can include:
- fixed software steps,
- tool calls,
- AI reasoning,
- specialist agents,
- human review,
- and safe stopping points.
In plain English, this means the product can say:
This step always happens first.
This tool is only allowed here.
This action needs approval.
This error should stop the workflow.
This part is where AI should reason.
That is a much healthier way to build agentic products.
The problem with giving the agent everything
A lot of early agent designs start with too much freedom.
The agent gets a goal, a prompt, some tools, and a broad instruction to complete the job.
That might be fine for research, drafting, or internal experiments.
But customer-facing product flows are different.
If an agent is handling money, access, records, messages, files, or support actions, the product needs stronger boundaries.
The problem is not that the model is useless. The problem is that the model is being asked to do too many jobs at once.
It has to understand the user.
Pick the next step.
Remember the business rule.
Choose the tool.
Interpret the tool result.
Decide whether to continue.
Handle the failure.
And then decide when the job is done.
That is a lot to put inside one flexible system. Good software usually separates responsibilities. AI workflows should do the same.
The simple product lesson
Some parts of a workflow should be flexible. Some parts should not.
AI is useful when the input is messy.
For example:
- understanding a customer complaint,
- summarizing a long thread,
- extracting details from a document,
- classifying user intent,
- drafting a reply,
- or explaining a confusing case.
But the product should control steps that need consistency.
For example:
- checking permissions,
- validating required fields,
- confirming account ownership,
- checking refund policy,
- blocking unsafe actions,
- asking for human review,
- saving audit records,
- and stopping when required data is missing.
These are not places where the model needs freedom.
These are places where the product needs control.
A better way to design an AI agent workflow
Think of the AI agent as one part of the product, not the whole product.
A stronger workflow might look like this:
- The customer sends a messy request.
- The product collects the required account context.
- The product checks permissions.
- AI interprets the request.
- The product checks the allowed path.
- AI drafts the response or next step.
- A human reviews sensitive cases.
- The product completes only the approved action.
- The system logs what happened.
That is still an AI-powered workflow.
But it is not a loose agent guessing its way through the business process.
It is a product flow with AI inside it.
That difference matters.
Example: a refund workflow
Imagine a customer asks for a refund.
A loose agent might read the message, check order history, decide whether the case sounds valid, and trigger a refund if it seems right.
That is risky.
A better workflow separates the jobs.
The product fetches the order.
The product checks the refund window.
The product verifies account ownership.
The AI reads the customer message and identifies the reason.
The product decides whether the case matches an approved path.
If the case is sensitive, a human reviews it.
Only then does the product complete the action.
The AI still helps.
But it does not own the whole process.
Why this matters for SaaS and AI products
More SaaS products are going to add agents.
Support agents.
Sales agents.
CRM agents.
Finance agents.
HR agents.
Operations agents.
Internal workflow agents.
Some will be impressive in the first version.
But the useful version is the one that can be trusted inside a product workflow.
That means the team should know:
- what the agent can read,
- which tools it can call,
- which steps are fixed,
- where AI is allowed to reason,
- where human review happens,
- what actions are blocked,
- what happens when something fails,
- and how the team can trace the workflow later.
That is the part many AI features miss.
They focus on what the agent can do.
The better question is what the product should allow it to do.
A practical rule
Here is a simple rule for product teams:
Use AI for unclear input.
Use software for clear rules.
Use humans for sensitive judgment.
That one line can prevent a lot of messy agent design.
If the task needs interpretation, AI can help.
If the task needs sequence, permission, or policy, the product should control it.
If the task affects money, access, trust, or customer outcomes, review should be part of the path.
Founder takeaway
Google ADK 2.0 Workflows is not only a developer update.
It is a signal about where AI product architecture is going.
The best AI agent is not always the one with more autonomy.
Sometimes the better agent is the one inside a clearer product path.
Because production AI is not about making the model responsible for everything.
It is about giving the model the right job, giving the product the right control, and making the whole workflow easier to trust.
Before adding more tools to an agent, ask this:
Which parts of this process should the AI never control on its own?
That is where the better design usually starts.
Sources
Google ADK 2.0 documentation - https://adk.dev/2.0/
Google Developers Blog: Why we built ADK 2.0 - https://developers.googleblog.com/why-we-built-adk-20/
Gemini Enterprise release notes - https://docs.cloud.google.com/gemini/enterprise/docs/release-notes
Top comments (0)