AI Agents for Product Managers

Product Managers would like to spend time discovering opportunities, defining strategy, and innovating. In practice, they spend hours writing documents, preparing meetings, and processing information.

AI agents can change this equation.

What Are AI Agents?

Systems that act proactively, make plans, and execute tasks using business context. They access real-time data and take concrete actions.

Chatbots answer questions. They are reactive, single-session.

Automation follows fixed rules. If-then, no judgment.

AI Agents plan, decide, act. They are proactive, continuous, autonomous.

In Practice: What Is the Difference?

Scenario	ChatGPT/LLM	Automation	AI Agent
Market research	You ask, it answers once	Not applicable	Researches automatically, consolidates, updates you
Prepare meeting	You ask for a client summary	Sends reminder	Checks calendar, researches participants, sends briefing to Slack
Analyze feedback	You paste text and ask for analysis	Moves tickets between columns	Monitors channels, categorizes, identifies patterns, creates report
Update roadmap	You ask for suggestions	Syncs dates	Analyzes metrics, suggests reprioritization, prepares communication

The fundamental difference: Agents work for you in the background. Chatbots work with you when you call them.

Use Cases for Product

Case 1: User and Market Research

Research is the natural starting point for agents because it is:

Repetitive: same steps, different sources
Time-consuming: consumes hours of manual work
Structured: follows clear patterns
Low risk: errors are easily correctable

A market research agent can:

Monitor sources (blogs, social media, G2, Product Hunt)
Collect competitor data (features, pricing, reviews)
Consolidate into weekly report in Notion or Slack
Alert about relevant changes

Typical result: From hours to minutes. The PM receives a consolidated briefing before each important decision.

Case 2: Documentation and Communication

PRDs and User Stories: The agent creates drafts based on call transcripts, existing tickets, company standards, and technical documentation.

Release Notes: Generates changelog from Git, transforms into customer language, adapts for different audiences, distributes to correct channels.

Meeting Preparation: Researches interaction history, summarizes open tickets, identifies attention points, sends briefing via Slack.

Status Reports: Collects product metrics, pulls sprint status, consolidates into executive format, sends to stakeholders.

Case 3: Feedback Analysis and Voice of Customer

Feedback comes from all sides: Intercom, NPS, reviews, recorded calls, support tickets, social media. Processing everything manually is impossible. Ignoring it means losing insights.

Agents can monitor all channels 24/7 and deliver:

Automatic categorization by theme
Sentiment analysis
Identification of emerging patterns
Critical problem alerts

Product teams use agents to identify top requested features, discover integration gaps, see where users are getting stuck, and generate automatic weekly reports.

Tools to Create Agents (No Code)

Zapier Agents (Beginner): Automation with AI that makes decisions. Familiar visual interface, 7000+ integrations.

Lindy AI (Beginner): Agents for email, calendar, and research. Focused on personal and team productivity.

Gumloop (Beginner): Visual builder with “Gummie” assistant. Creates agents by describing in natural language.

Cassidy (Intermediate): Enterprise agents with company context. Connects with internal knowledge bases.

Relevance AI (Intermediate): No-code platform for custom agents. Allows creating more complex workflows.

ChatPRD (PM-Specific): Specialized in product documentation. PRDs, specs, user stories with PM context.

Tip: Start with one tool, one use case. Expand after validating value.

Best Practices

The magic question: “What would I delegate to a well-trained junior intern?”

If the answer is “this here,” it is probably a good use case for agents.

Start small: One agent, one task, one channel. Expand later.
Avoid automatic decisions: Prefer suggestions, drafts, alerts. Human decides.
Maintain visibility: Receive notifications of what the agent did. Audit.
Use real data: Connect with your sources of truth, not generic data.
Iterate fast: Agents improve with feedback. Adjust the prompts.

What to Avoid

Learning Curve: Many platforms require technical reasoning. Start with simple no-code tools like Zapier or Lindy.

Over-delegating: Use AI to organize, but keep your head connected to what customers are saying. Read original data regularly.

Generic Agents: Using AI trained on generic data for specific questions about your business generates disappointment. Connect agents with your company’s data.

Excessive Autonomy: Letting agents make decisions alone can create problems that are difficult to reverse. Prefer suggestions to automatic actions.

Where to Start: Your First Agent

Identify: What repetitive task consumes the most time in your week?
Validate: Is it structured? Low risk? Would you delegate to an intern?
Experiment: Choose a tool, create the agent, run for 1 week
Iterate: Adjust prompts, expand scope, add integrations

Suggestion to start: Create a meeting preparation agent. Integrates calendar + CRM + Slack. Visible value in 1 week.

The Future: AI as Coworker

In 1-2 years, having an agent that takes care of parts of your work will be as normal as using Google Docs or Trello.

Agent market in 2025: $7.6 billion. Projected annual growth: 49.6%.

What changes for PMs:

More strategic time: Operational work delegated to agents
Broader vision: Agents process more data than humans can
Faster decisions: Consolidated information when you need it

Key Points

Agents act, chatbots respond. Agents work in the background, proactively.
Start with low-risk repetitive tasks: research, meeting preparation, feedback consolidation.
No-code tools already exist: Zapier Agents, Lindy, Cassidy, Gumloop.
Keep human in control: suggestions, not automatic decisions.
Connect with your real data: generic agents disappoint.
Start this week: one agent, one task. Value in days, not months.

At Victorino Group, we help companies implement governed AI agents that actually work. If you want to free your product team for strategic work, let’s talk.