What’s the Difference Between a Real AI Agent and a ChatGPT Wrapper?

Nilkanth Patel, Head of Product Engineering

4min read

AI can write a decent email, read a PDF, and even paint a picture. But can it survive a day in the chaos of live logistics? Almost never. MIT’s latest study reveals 95% of AI deployments fail, and the number one reason is simple: real work is messy.

Every operation carries decades of tribal knowledge, customer-specific edge cases, and unwritten rules. We’ve seen brokerages averaging 20 custom rules per customer across 200 customers. That’s 4,000 rules an AI needs to master just to keep up. It’s the difference between giving a loyal customer a free chassis rental and accidentally overbilling them.

In theory, AI should capture every rule, apply the right one at the right time, double check its work, and raise its hand when it’s unsure.

In practice, today’s deployments fall flat. The models exist. The missing piece is the technology platform to manage the models, enabling memory, iterative reasoning, and exception handling. Without it, even billion-dollar models don’t last a week in production.

CoPallet was designed to fill this gap: to manage memory, retrieve context, and reason reliably inside the messiness of real work.

Memory Layer Captures the Chaos

CoPallet converts SOPs into thousands of memories

Think of the Memory Layer as the brain of the operation. It’s where every SOP, every edge case, every “this is just how it works” lives. If you have written rules, CoPallet can ingest them. If you don’t, it can sift through system logs and past transactions to draft a v1 of your SOP.

From there, those rules get broken into thousands of “memories”, discrete facts like “Auto-approve 5% discounts for Gold customers” or “Chassis rentals are $40/day, two-day minimum.”

The key difference between memories and business logic in traditional automation platforms is that CoPallet stores memories in plain English. Traditional systems with complex multi-step workflows are slow to deploy and update. All CoPallet needs is a SOP to form memories.

The Memory Layer automatically generates a "taxonomy," an organization system where memories by classified by topic, customers, and locations. For example, accessorial rules about fuel surcharges and chassis rentals would be in the same topic cluster.

Reasoning Layer Thinks Before Acting

Deep Reasoning means CoPallet checks its work before executing

Once the brain is stocked with memories, the Reasoning Layer goes to work. An incoming email for an LTL load triggers a search for every memory tied to that customer’s LTL process. The AI follows those instructions, drafts a response, and then submits it to a “judge,” another AI model trained to verify that all the memories have been correctly applied. Have the addresses been validated? Do the pickup and delivery windows overlap with location operating hours?

If the draft passes, it moves forward. If not, it gets sent back with detailed feedback for revisions. If CoPallet encounters an unfamiliar or undocumented scenario, it escalates it to a human operator. This back-and-forth iteration process is called Deep Reasoning. Deep Reasoning gives operators the confidence that CoPallet will check its own work to perform multi-step workflows reliably.

To further improve reliability, the Reasoning Layer uses two other techniques:

Multi-model approach

The reasoning is done by multiple models from different providers, including OpenAI, Google, Anthropic, each working independently, like having multiple employees compare answers before sending them to the customer.

Historical inference

CoPallet will look at previous shipments in the TMS to infer future patterns. For example, if handwriting for a pickup address is difficult to read, CoPallet will look at recent pickup addresses from this shipper in the TMS to see if there is a probable match, before escalating to an operator.

Interaction Layer Executes

CoPallets executes within your existing systems

The final step is execution. Whether that means sending the email, pushing an API call, navigating through a third-party portal, or making a voice call, CoPallet can integrate with your TMS, WMS, ERP, or anything else you use.

Why Memory and Reasoning Matter When Evaluating AI

All automation tools can perform the functionality in the Interaction Layer. Even voice is a rapidly maturing technology with vendors like OpenAI, ElevenLabs, Bland, and Cartesia all able to generate realistic outputs.

What really sets logistics AI vendors apart is under the hood, their ability to retrieve the right memories and reason through multi-step problems. The operators who succeed will be the ones who deploy AI agents that can handle nuance, context, and complexity without missing a beat.

Want to see how CoPallet can learn and execute your workflows? Reach out to an AI deployment strategist for a free consultation.

See CoPallet in Action