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·7 min read·Seven Hills Team

Building Agentic AI Products: Our Approach to Autonomous Software

aiagenticproductengineering

We're building an agentic organization. Not just using AI as a feature, but designing products where AI agents do real work — autonomously, reliably, and at scale.

This is a fundamentally different approach from traditional software. Here's how we think about building AI-powered products.

What "Agentic" Actually Means

The term gets thrown around loosely. For us, an agentic product has three characteristics:

  • Autonomous execution — The AI doesn't just suggest; it acts. It completes tasks without constant human intervention.
  • Goal-oriented behavior — Given an objective, the agent figures out the steps. It adapts when things don't go as planned.
  • Continuous operation — Agents work in the background, monitoring, processing, and responding to events over time.

StoreBlog doesn't just help merchants write content — it researches topics, drafts posts, optimizes for SEO, and publishes on schedule. StoreWorkers doesn't just provide templates — it deploys AI teams that handle customer support, inventory alerts, and marketing tasks automatically.

The Trust Problem

The biggest challenge with agentic AI isn't technical — it's trust. Users need to believe the agent will do the right thing when they're not watching.

We've learned to build trust through:

Transparency. Every action the agent takes is logged and visible. Users can see what happened, when, and why. No black boxes.

Guardrails. Agents operate within defined boundaries. They can publish a blog post, but they can't change pricing. They can respond to reviews, but they escalate complaints. Clear limits reduce anxiety.

Gradual autonomy. New users start with approval workflows. The agent drafts, the human approves. As trust builds, users unlock more autonomous operation. This progression feels natural and safe.

Easy reversal. Anything the agent does can be undone. Published a post that doesn't fit? One click to unpublish. This safety net encourages experimentation.

Designing for Agents, Not Features

Traditional software design starts with features. What can the user do? What buttons do they click? What forms do they fill out?

Agentic design starts differently. We ask: What job does the user want done? What would a competent assistant do? How do we get out of the way?

This leads to different interfaces:

  • Dashboards over forms — Users monitor agent activity, not input data manually
  • Conversations over menus — Natural language instructions replace complex configuration
  • Outcomes over actions — Users define goals; agents determine the steps
  • Exceptions over routine — Humans handle edge cases; agents handle the 90%

The Architecture of Agency

Building reliable agents requires different architecture patterns than traditional CRUD apps.

Task Queues and State Machines

Agents need to track complex, multi-step workflows. A blog post goes through research → outline → draft → review → publish. Each step might succeed, fail, or need human input. We model these as explicit state machines with persistent state.

Retrieval and Context

Agents need to know things. What's the brand voice? What products exist? What's been published before? We build retrieval systems that give agents the context they need to make good decisions.

Evaluation and Feedback Loops

How do you know if an agent is doing well? We build evaluation into the product. Track what gets approved vs. rejected. Measure outcomes like engagement and conversions. Feed this back into agent improvement.

Graceful Degradation

AI services have outages. Models have bad days. Rate limits exist. Agentic products need to handle these gracefully — queue work for retry, fall back to simpler approaches, alert users when intervention is needed.

Where AI Fits (And Doesn't)

Not every product needs to be AI-powered. We're deliberate about where AI adds value.

AI shines when:

  • The task is repetitive but requires judgment
  • Scale is impossible with humans alone
  • Personalization at the individual level matters
  • The cost of mistakes is low and recoverable

AI is overkill when:

  • Simple rules would work fine
  • The task is already fast and easy for users
  • Accuracy requirements are extremely high
  • The domain is too narrow for models to help

Our Chrome extension BlurShield doesn't use AI — it's a privacy tool that blurs sensitive content. AI would add complexity without adding value. The problem is solved better with simple, fast, deterministic code.

Our Shopify apps are deeply AI-powered because content creation and automation at scale is exactly where AI excels.

The Agentic Future

We believe most software will become agentic over time. Users don't want to use tools — they want outcomes. The best products will be the ones that deliver those outcomes with minimal user effort.

This doesn't mean AI everywhere. It means thoughtful application of AI where it genuinely helps, combined with simple, reliable tools where it doesn't.

We're building that future — one product at a time.

Seven Hills Software builds AI-powered products for businesses. Our Shopify apps under Chakril help merchants automate content and operations. Reach out at hello@sevenhillshq.com if you're building in this space.