Human-in-the-Loop Learning in a Production-Ready AI Agent

By building AI agents used by millions of people every month, I started to notice recurring patterns in where and why agents fail in production.

One of the most common causes is: missing or incomplete information from the business itself (owner or operator of the agent). Even with the most thorough onboarding imaginable: ingesting internal documentation, crawling public sources, and encoding explicit business rules, you will never have enough information to cover the millions of real-world scenarios an AI agent will face once deployed.

This gap between design-time knowledge and production reality is not an implementation flaw; it is a structural limitation.

From a developer’s perspective, the solution initially appears straightforward: give the agent more context. When I first encountered this issue through customer support feedback, my response was simple and technical: this is not a failure of the AI agent; the business never provided this information, so the agent cannot possibly know it.

However, this line of reasoning breaks down in production. From a user perspective, this manifests just as broken trust.

How do we solve this issue? - Human-in-the-Loop Learning

A 2 step solution for this problem 

Missing-Information Acknowledgement Pattern

When an AI agent lacks the information required to answer a user query, the correct behavior is not to hallucinate or deflect, but to explicitly acknowledge the gap and initiate a resolution workflow.

In this pattern, the agent:

1. Detects that the requested information is not present in its knowledge base
2. Transparently informs the user that the information is currently unavailable
3. Optionally collects contact details to follow up once the information is provided
4. Notifies the merchant with a structured request for the missing information
5. Incorporates the newly provided information into its knowledge so future queries can be answered correctly

Example

• A user asks: “Will the shop be open during the Christmas holidays?”
• The agent searches its available knowledge sources but finds no information related to holiday opening hours
• The agent informs the user that this information is currently unavailable and offers to follow up once it is confirmed
• In parallel, the agent notifies the merchant: “A user asked about Christmas holiday opening hours. Can you confirm whether the shop will be open and, if so, on which dates?”
• Once the merchant provides the information, the agent updates its knowledge and can reliably answer all similar future queries

Merchant-Initiated Feedback and Answer Correction

Not all failures originate from missing information detected at query time. In many cases, the agent produces an answer that is technically plausible but business-incorrect. In these situations, the most effective intervention is merchant-initiated feedback on a real production interaction.

In this pattern, the merchant actively supervises the agent’s outputs and corrects them when necessary.

How it works

1. The agent responds to a user query using its current knowledge and policies
2. The merchant reviews the interaction—typically through an admin or monitoring interface
3. The merchant flags the response as incorrect or suboptimal (negative feedback)
4. The merchant provides a corrected answer and, optionally, guidance on how the case should be handled in the future
5. The system incorporates this feedback so the agent can produce the corrected behavior in similar scenarios going forward

Example

• A user asks: “Can I return a product after 30 days?”
• The agent answers based on outdated or ambiguous policy information
• The merchant notices the response and marks it as incorrect
• The merchant provides a revised answer: “Returns are accepted within 14 days, except for personalized items.”
• The system updates the agent’s knowledge or reward signals so future return-related questions follow the corrected logic

Why This Matters

Together, these two patterns transform production failures into structured learning signals.

Instead of treating incorrect or incomplete answers as isolated incidents, the system uses them to continuously align the agent with the business’s evolving rules, policies, and expectations. Missing information becomes an explicit request for clarification, while incorrect answers become opportunities for correction and reinforcement.

The result is an AI agent that does not merely respond—it learns in production, reduces repeated failure modes, and improves over time without requiring constant re-prompting, manual rule updates, or full retraining cycles.

How we integrate this in the agent life cycle

The Knowledge Base as Structured Memory

All merchant-provided inputs—both corrective feedback and missing information—are embedded into a shared Knowledge Base.

Architecturally, this Knowledge Base is a vector store used for retrieval-augmented generation (RAG). It contains merchant-specific information spanning personalization, operational rules, and domain knowledge. For this article, we focus specifically on the subset storing HITL feedback.

Conceptually, different memory entries influence different stages of reasoning:

• Pre-response grounding and context enrichment
• Policy constraints and behavior shaping
• Retrieval, ranking, and validation phases

This separation allows the agent to learn where and how to apply human feedback, rather than injecting it indiscriminately into every response.

Conclusion

AI agents do not fail because they are poorly designed; they fail because production environments are inherently incomplete and constantly changing. No onboarding process, knowledge base, or prompt can anticipate every real-world scenario a business will face.

Human-in-the-Loop learning provides a pragmatic solution to this limitation. By allowing agents to acknowledge missing information and by enabling merchants to correct real production answers, feedback becomes part of the system rather than an external patch.

The result is an AI agent that improves through use, aligns more closely with business reality over time, and earns user trust not by pretending to know everything—but by learning when it does not.