AI in ecommerce and logistics is moving fast. What started with simple prompts has quickly evolved into something much more powerful and much more complex.
If you’re exploring AI for logistics or ecommerce AI, you’ve likely come across terms like prompt engineering and context engineering. But there’s a newer concept that matters even more as teams move from experimentation to production:
Harness engineering.
This is the shift that turns AI from a helpful tool into something your team can actually rely on.
What is Harness Engineering?
To understand harness engineering, it helps to look at how AI development has evolved:
- Prompt engineering focuses on crafting better prompts to improve outputs
- Context engineering ensures the model sees the right information at the right time
- Harness engineering goes further and designs the entire system around the AI agent
At its core:
Harness engineering is the discipline of designing the environment, guardrails, and feedback loops that allow an AI agent to operate autonomously and reliably.
Instead of asking:
- “What should the model see?”
You’re asking:
- “How do we build a system where the model can consistently find and use the right information on its own?”
This becomes critical in ecommerce and logistics, where AI agents are expected to:
- Handle operational complexity
- Interact with multiple systems
- Support different teams and workflows
- Make decisions with real business impact
Why Harness Engineering Matters for Ecommerce AI
As AI models become more capable and more accessible, the real advantage is no longer the model itself. It’s how you use it.
For ecommerce brands and logistics teams, that means:
- Faster decision-making across shipping, routing, and checkout
- More efficient internal workflows
- AI that adapts to different roles across your organization
But without the right structure, AI agents can:
- Pull the wrong data
- Use the wrong tools
- Deliver inconsistent or incomplete responses
Harness engineering is what solves that.
How Passport Applies Harness Engineering
At Passport, we’ve moved beyond basic LLM usage to building AI agents that support real operational workflows across teams.
Here are three practical ways we’ve implemented harness engineering.
1. Skill Indexing: Helping AI Choose the Right Tool
Our AI agent has access to hundreds of specialized skills, from shipping logic to internal tools.
Initially, it struggled to consistently choose the right one.
The solution: a skill index.
We created a structured lookup system that maps:
- Trigger words
- Use cases
- Relevant skills
Instead of loading everything into context, the agent now:
- Identifies the intent
- Selects the correct skill directly
Result: Correct skill usage improved by 30%
For logistics AI use cases, this is especially important when dealing with:
- Shipping routes and carriers
- Service levels and delivery options
- Checkout and rate calculations
## :ship: Shipping & Logistics
| Trigger words / situations | Skill | Path |
|—|—|—|
| shipping routes, carrier, lanes, service levels, routing | shipping-routes | shipping-routes/ |
| Shopify checkout, test cart, shipping rates, checkout agent | shopify-checkout | shopify-checkout/ |
2. User Profiles: Personalizing AI Across Teams
AI in ecommerce isn’t used by just one type of user. It supports multiple teams, each with different goals, workflows, and expectations.
You might have:
- Operations teams focused on shipping and routing
- Product managers testing flows and features
- Customer experience teams handling edge cases
- Marketing teams looking for insights and performance data
Each group needs something different from the same AI system.
To make this work, we built dynamic user profiles that allow the agent to understand who it’s talking to and adjust accordingly.
How it works
Every user the agent interacts with gets their own profile, stored in a simple, structured format. This gives the agent a persistent layer of memory beyond a single conversation.
Passport has many employees interacting with the agent every day. It need to understand who it’s talking to, and what being genuinely helpful to that person means. We store user identities in their own file path where the agent has a summary for them and can form memories about that person.
users/
│ ├── U0XXX.md
│ ├── U7XXX2.md
│ ├── U0XXX.md
│ └── …
Each file contains a lightweight summary the agent can reference and update over time. These profiles are often generated by the agent itself based on early interactions.
Example:
# User Profile: Ilan
– **Name:** Ilan
– **Language:** English
– **Style:** Direct, concise
– **Role:** Passport team member
– **Notes:**
– Reached out via Slack DM
– Prefers straightforward task-oriented help
When a user sends a message (in our case, via Slack), the agent:
- Identifies the user by ID
- Checks if a profile already exists
- Creates or updates the profile as needed
- Uses that context to shape its response
Why this matters
Instead of treating every interaction the same, the agent can:
- Adjust tone and level of detail automatically
- Prioritize what matters based on the user’s role
- Build memory over time to improve future responses
This is especially important in ecommerce and logistics, where the same question can require very different answers depending on who’s asking.
Impact
Adding user-aware context led to:
- Faster adoption across teams
- More relevant, actionable responses
- Higher daily engagement and repeat usage
Most importantly, the agent starts to feel less like a tool and more like a teammate that understands how you work.
3. Enforcing “Taste”: Making AI Feel Like Your Team
Accuracy isn’t enough. How the AI communicates matters just as much.
We define “taste” as:
- Tone
- Communication style
- Level of detail
- Quality standards
At Passport, we encode this directly into the agent’s guardrails.
This includes rules like:
- Always provide sources when making claims
- Avoid incomplete or rushed responses
- Match the tone of a Passport team member
- Keep communication helpful and professional
– Don’t be mean. Sarcastic and playful ≠ cruel.
– Always include source links when making claims from web search.
– Use anchor links when available — link to the specific section, not just the page.
– Never send half-baked replies to messaging surfaces.
Why this matters:
Most internal AI tools fail because they feel like… tools.
When an AI agent communicates like a real teammate:
- People trust it faster
- Adoption increases
- It becomes part of daily workflows
When Do You Need Harness Engineering?
If you’re only using a basic chatbot with a single prompt, you likely don’t need this yet.
But you’ve crossed the threshold when your AI:
- Uses multiple tools or integrations
- Supports more than one team
- Operates with some level of autonomy
In ecommerce and logistics, that threshold comes quickly, often within weeks of moving beyond a prototype.
The Future of AI for Logistics and Ecommerce
The evolution is clear:
- Prompt engineering made AI useful
- Context engineering made AI smarter
- Harness engineering makes AI reliable
For teams investing in AI for logistics or ecommerce AI, this is the difference between:
- A demo and a production system
- Occasional use and daily reliance
- Experimentation and real business impact
At Passport, we’re focused on building AI systems that don’t just work, but work consistently in the environments our teams depend on every day.
Build AI That Works Across Your Ecommerce Operations
AI is only as powerful as the system behind it. With the right structure, it becomes a reliable part of how your team operates every day.
At Passport, we’re building tools that help ecommerce brands scale globally with smarter automation across logistics, shipping, and operations.
Here’s what you can expect:
- More reliable decision-making across your logistics workflows
- Automation that adapts to your team, not the other way around
- AI that fits directly into how your business runs
Talk to our team about how Passport is leveraging AI to accelerate international growth for the brands we support.
Authored by Ilan Rotenberg
Senior Director, Product | Passport
Ilan Rotenberg, a seasoned engineering and product pro, boasts six years in software product management. Fueled by a passion for elevating user experience, Ilan excels in unraveling user problems, fostering product adoption, and streamlining customer flows. Armed with a Master’s in Mechanical Engineering from the University of British Columbia, Ilan has left an indelible mark, co-creating products with clients such as Airbus, the United Nations, Toyota, Rhode Beauty, and Clove.