The Rise of Agentic Apps: Autonomy Baked In

Most of today’s conversation about AI agents is about orchestration. Picture a meta‑assistant that hops between your apps, clicks the buttons you would have clicked.  That narrative is useful, but incomplete. The next wave is Agentic Apps: software with agents inside the product so it can perceive, decide, and act on your behalf.

What We Mean By “Agentic AI”

First, let’s agree on a definition –  agentic AI refers to systems that pursue goals, reason about plans, adapt to feedback, and take real actions under constraints. Four traits matter.

• Proactive: they initiate steps toward a goal rather than waiting for a prompt.

• Adaptive: they update plans as context changes.

• Reasoning: they break problems into subgoals and make decisions based on context.

• Autonomous: they can execute safely without constant supervision, within guardrails you set.

The Current Narrative is Only Half the Story

Two ideas currently dominate.

1. Agents sit outside products and tie them together. This gets a lot of enterprise airtime because orchestration across CRMs, ERPs, and data lakes is a big win and is where most buyers are spending.

2. Agents are primarily enterprise tools. Analysts and vendors keep describing “agents as apps” for enterprise suites, which reinforces the idea that this is mostly a top‑down IT play. 

Both are true, but the overlooked opportunity is what happens when agents live inside the product itself.

Why Agents Belong Inside Products

When you embed agents within a product, you get capabilities that external orchestrators can’t match.

1. Market expansion – Agentic products offer huge potential to move beyond enterprise use cases to target consumer and B2B users. We’re already starting to see the latter take shape with customer service platforms like Intercom and productivity apps like Notion. Consumer will undoubtedly be next 

2. Better user experience – Agentic products inherit your product’s polished UX, permissions, and state. Users get “it just works” rather than DIY glue code. 

3. Deeper data and safer action – In‑product agents can use first‑party telemetry, domain models, and private data the product already stores. 

4. Aligned economics – Outcome‑based pricing becomes possible. For example, Intercom’s Fin charges per resolved conversation.

   
   

Types of In-App Agents

In-app agents can look very different depending on the product’s role and audience. It's early, we’re seeing three distinct patterns emerge.

1) Set‑and‑Forget

With this approach the product embeds an agentic workflow and runs it continuously on the user’s behalf.  For example, our product AsqMe uses agents to help content creators answer and monetize audience questions at scale. Once a user connects their YouTube account, agents go to work drafting answers to incoming questions, pulling questions from viewer comments and inserting the answers back into the comment threads. 

2) Configurable

Unlike Set-and-Forget, here the product exposes the workflow to the user. The key is that the workflow is native to the product’s UI and data, not a Zapier‑style external glue layer. For example, travel site Kayak recently launched Kayak.ai, an agent that lets you specify travel constraints and fetches lodging, car rental and airline options.  

3) On‑Call 

A copilot that goes beyond chatbot-type suggestions and guidance – it carries out tasks and reports back. The agent is standing by, so it fits workflows where an agent assist might come in handy but isn’t necessarily a core feature.  For example, Hubspot’s Breeze Assistant can customize CRM properties on-demand, drudgery that can take 30 minutes or more depending on your level of knowledge. 

Design principles for Agentic Products

Like any new capability, in-product agents work best with clear design rules. These principles can help teams avoid pitfalls and unlock maximum value.

1. Intent over prompts – Your UI should capture a durable goal and clear constraints so the agent can plan and act repeatedly without constant input. This shift turns the agent from a reactive tool into a proactive partner that works toward your objectives over time.

2. Explainability as UX –  Show users the agent’s overall plan, status and next action so they can supervise without micromanaging. Provide previews and require confirmation for risky actions.  Always show the changes made by the agent and enable rollbacks.

3. Guardrails and roles – Give agents only the minimum access needed to do their job. Use clear limits—time windows, spending caps, and policy checks—to prevent overreach and maintain safety. 

4. Memory with forgetting – Long‑lived memory helps, but retention policies should mirror your product’s privacy posture.

5. Human in the loop  – Keep the user in control. The product must show the agent’s plan, ask before any high-impact action, and make it one click to take over or undo.

   
   

How to Get Started

Embedding an agent into your product isn’t about flipping a switch—it’s about picking the right starting point, defining clear boundaries, and building confidence through small, high-impact wins. The following steps will help you move from concept to in-product reality.

• Pick a use case – Target one high-value, high-pain workflow, and let the agent deliver the finished result.

• Start small and scale with proof – Pilot with a small group of users, measure results, and expand when the agent consistently meets your success criteria.

• Start with oversight – Begin your testing with clear checkpoints for reviewing and approving the AI’s actions. Let the AI act on its own only after it repeatedly proves reliable.

• Set guardrails – Decide in advance which actions are off-limits or irreversible to protect trust and safety.

• Log everything – Track the agent’s plans, tools used, data accessed, approvals, and results for analysis and improvement.