If your AI agent is performing mission-critical tasks that demand high levels of accuracy, you probably need humans in the loop (HITL) to agree on the constraints and definitions of your domain knowledge, so that AI can reason over it more reliably.
Unless subject-matter experts (SMEs) are involved at the foundational level, we find that errors and hallucinations can overwhelm human reviewers. This creates bottlenecks that undermine trust and prevent AI services from scaling.
What is human in the loop AI (HITL)?
Human-in-the-Loop (HITL) AI is where people – usually domain experts – actively take part in the AI lifecycle. This tends to involve reviewing and guiding AI outputs or decisions, and can vary from active human intervention, to more of a monitoring role, which is referred to as humans-on-the-loop (HOTL). In general, humans provide the nuanced judgement and real-world experience that large language models (LLMs) cannot currently match.
HITL can also be a compliance requirement in regulated domains. The EU AI Act, for example, mandates effective and proportionate human oversight for high-risk systems, such as healthcare, recruitment, critical infrastructure and law enforcement.
How is HITL deployed in AI?
In vertical AI systems, HITL is often used during the training and development phase, for reinforcement learning or defining domain knowledge. During validation and testing, humans can review edge cases, simulate real-world scenarios and provide feedback to refine models. During production or runtime, humans can approve, edit, or perform an override before final action is taken by an AI agent.
Humans are also often required to update and maintain their organisation’s knowledge over time, as new concepts and processes surface in the domain.
Can human-in-the-loop make AI more trustworthy?
We find that people trust an AI agent better when they can understand why it is doing or deciding something in a certain way. In other words, AI agents need to be explainable so that even non-technical people can verify their outputs, but also learn from them and preserve human skills that might otherwise be lost.
That trust can be heightened further when SMEs define the concepts, rules and policies that ground the AI agent. In our experience, this sense of ownership over an AI service also drives adoption, and motivates users to provide the feedback that ensures an agent can continually improve.
In most enterprises, HITL tends to become more selective and exception-based as reliability improves and trust in the AI system rises. This is when productivity benefits can really become transformative.
How can HITL scale?
HITL is more likely to scale if humans are in the loop before runtime. This is where SMEs can help ground the domain’s knowledge, defining the rules and procedures that the AI must follow before it ingests the data.
Other approaches use HITL after runtime, as part of a firewall to protect against errors and hallucinations. In these cases, AI can escalate low-confidence, high-risk, or edge-case items to humans, as a way of preventing cognitive overload at scale. Controlling how AI makes these judgements is not straightforward however. Reinforcement learning is one approach, which can be based on preference data or rankings supplied by humans.
What are the drawbacks of HITL?
HITL can be expensive and slow compared to automation. Humans can be inconsistent, and prone to errors or bias. Key people cannot be available 24 hours a day, and get tired or bored. Where sensitive information is involved, privacy and security issues can arise.
The other problem with HITL – frequently underestimated in our experience – is expert disagreement. AI cannot learn a single correct answer when experts disagree, and those experts may encode different policies and values that end up contradicting one another. A 2026 paper by Konstantinos Lazaros et al. highlighted the problem of conflicting human feedback as part of its review of collaborative intelligence in safety-critical applications.
To help resolve human disagreement among SMEs, consensus-building platforms have emerged which specialise in aligning experts on the things that matter in their domain, as a means of structuring domain knowledge for AI to reason over. Even here, care needs to be taken not to suppress minority views.
What is the future of human-in-the-loop?
The acknowledged problems of HITL need to be weighed against the expense and risks involved in running large language models at scale without adequate supervision or constraints. Many Silicon Valley businesses have put all their faith in end-to-end automation – even though returns on investment in AI have continued to disappoint. Alex Karp, CEO of Palantir, describes this as the ‘AI lag’, and attributes it to intelligent systems that are not sufficiently reliable for enterprise use.
The issue of unreliability is already forcing several companies we know, to bring back human participation in their agentic plans. A more collaborative approach between humans and AI agents, that plays to their respective strengths, may well prove to be the missing link that delivers meaningful ROI to all those billions being spent on LLMs and compute.