From our experience testing and improving 100+ AI agents with our subject matter experts, we've learned something critical: hallucinations aren't bugs and they are not going away.

Every time we ask an AI system a question, it's predicting what words should come next — which is quite different from verifying the truth. However, this is not a failure. That's what the system does by design.

As long as we're building systems on token prediction, fabricated information is pretty much guaranteed. But here's the milestone we've reached: we now understand the root causes well enough to architect around them and keep improving over time.

The organizations that are getting AI right in 2026 are not waiting for the perfect model. They are focused on building:

• Verification and correction systems that catch errors before users see them;

• Governance frameworks and training that make humans accountable; and

• Quality processes that turn unreliable technology into trustworthy business tools.

If you're charged with implementing AI, overseeing AI governance, or making decisions about AI adoption, this is your guide to separating signal from noise among people stuck in fear, afraid to implement anything at all. We are all seeing it in the news — the cost of discovering AI errors in production is exponentially higher than preventing them at design. So our focus is on architecting solutions that work.

Why AI Hallucinates: Understanding the Root Causes

Let's start with clarity: when we talk about AI "hallucinations," we're describing instances where AI models generate false, misleading, or nonsensical information with complete confidence. Why does this happen? There are multiple failure points that can be tracked across the AI lifecycle.

Training Data: Garbage In, Guaranteed Garbage Out

Those of us working with data know the Garbage In–Garbage Out principle from long ago. A predictive model built on bad data can never develop value and becomes a liability.

In AI, the impact is tenfold. The quality of AI output can never exceed the quality of its training data. But here, we also have inherent training data issues, present by design. And the implications run deeper than most organizations realize.

LLMs are trained on vast amounts of internet data, which inevitably contains:

• Inaccuracies and contradictions;

• Outdated, missing or outright false statements and claims;

• Content that blurs the line between fiction and reality, that may have been built to persuade, not state the facts; and

• Gaps of granularity for locations or levels of expertise.

When your training data includes this mess, the model learns these flaws as patterns to replicate.

The looming data crisis: As models consume available high-quality training data, they increasingly rely on synthetic data (AI-generated training content). This threatens to amplify hallucination problems. When AI trains on AI-generated content, errors compound and quality inevitably degrades.

Model Architecture: Dream Machines by Design

We all know this, but often, when using output, forget the fact that LLMs predict the next most probable word based on statistical patterns learned from training data. They don't "understand" information. They don't verify the truth. They complete patterns that look statistically correct, even when factually wrong.

This isn't a flaw. It's the core operating principle.

LLMs lack:

• True world models;

• Common-sense reasoning capabilities; and

• Any inherent knowledge of truth.

When uncertainty exists, they don't admit ignorance. They guess, and often do so with overconfidence because training procedures reward guessing over acknowledging uncertainty.

Looking into the future, as AI models grow more complex — particularly reasoning models designed to "think through" problems step-by-step — they can become more prone to hallucination. This compounding happens because errors can occur at each step of their advanced thinking processes, multiplying the chances of incorrect conclusions.

Training and Evaluation: Rewarding the Wrong Behaviour

Standard training and evaluation procedures inadvertently reward AI systems for guessing rather than acknowledging uncertainty. Modelling benchmarks prioritize accuracy, penalizing expressions of doubt. This creates a vicious circle where models learn that guessing maximizes performance metrics.

How Users Trigger Hallucinations

Even well-trained models hallucinate more frequently under certain conditions:

• Vague or ambiguous prompts confuse AI, leading to fabricated responses;

• "Expert voice" prompts trigger convincing but fabricated information that users trust because it sounds authoritative;

• Recent events or niche topics increase hallucination rates, as they may not have any or sparse representation in the training data; and

• Lack of real-time validation against authoritative external sources, or even a judgement call on what constitutes a credible or a reliable source may be difficult to decipher.

Bottom line: As long as systems predict the next token probabilistically, hallucinations are inevitable. Models must output something even when uncertain. Without additional enhancements into the process, they cannot express "I don't know" or "insufficient data" on their own. Instead, they choose the most statistically plausible continuation, which may be factually incorrect.

When AI Gets It Wrong: Real-World Consequences

If you are unconvinced that hallucinations need to be studied and present a serious problem to AI implementation, we would like to highlight a few examples — some older and some more recent — of when errors escaped into production.

Case Study 1: Deloitte's $1.6 Million Newfoundland Health Care Report

The Failure: November 2025. Newfoundland and Labrador's government discovers that a 526-page health care workforce plan prepared by Deloitte, costing nearly $1.6 million CAD, contains false and non-existent citations.

The report includes:

• Erroneous citations for journal papers that never existed;

• Misattributions of real researchers to studies they'd never worked on; and

• Incorrect citations of researcher Gail Tomblin Murphy in a non-existent academic paper.

The Consequences: Premier Tony Wakeham calls the situation "embarrassing" and pledges to review the report in detail. The Registered Nurses' Union emphasizes: "Credibility matters. Transparency matters. Sound planning matters" and calls for responsible AI use. The provincial NDP calls for strict AI regulations, arguing that "the use of AI could change how decisions are being made, when the facts themselves that are presented are false or fabricated". Needless to say, value of consulting work comes into question with major financial ramifications and the trust eroded and difficult to repair.

What had to go wrong architecturally:

• No verification layer between AI generation and human review;

• Citations weren't validated against actual sources; and

• Quality control assumed AI outputs were accurate by default.

Case Study 2: Air Canada's Chatbot Creates Binding Policy

The Failure: In February 2024, the British Columbia Civil Resolution Tribunal rules Air Canada liable for misleading information provided by its chatbot regarding bereavement fares. Customer Jake Moffatt's grandmother dies in November 2022. Air Canada's chatbot tells him he can purchase a full-price ticket and apply for a retroactive refund for the bereavement rate within 90 days.

Relying on this advice, Moffatt books the flight. When he later attempts to claim the discount, Air Canada denies his request.

Air Canada's remarkable defence: They argue the chatbot is a "separate legal entity" responsible for its own actions.

The tribunal's response: Tribunal Member Christopher Rivers states it should be "obvious to Air Canada that it is responsible for all information on its website. It makes no difference whether the information comes from a static page or a chatbot".

What went wrong architecturally:

• The chatbot lacked the mechanism to check the actual company policy;

• There was no guardrail to verify generated responses against authoritative policy documents; and

• The system didn't flag uncertainty levels and defer to human agents for policy questions.

Case Study 3: Lawyers and Fabricated Legal Citations

The Pattern: Between 2023 and early 2026, numerous lawyers face sanctions for submitting legal documents containing fake citations generated by ChatGPT. Attorneys use AI for legal research, trust the output without verification, and file briefs citing non-existent cases.

The Consequences: In Mata v. Avianca, Inc., attorney Steven Schwartz and his colleague face sanctions for submitting fictitious cases to federal court. California attorney Amir Mostafavi is fined $10,000 after 21 of 23 quotes cited in his opening brief are fabricated by ChatGPT. The California appellate court notes this is one of the highest fines for AI fabrications in the state.

The Court's Warning: The California 2nd District Court of Appeal issues a clear warning: "Simply stated, no brief, pleading, motion, or any other paper filed in any court should contain any citations — whether provided by generative AI or any other source — that the attorney responsible for submitting the pleading has not personally read and verified".

The pattern is clear: All three cases share common architectural failures — no verification layer between AI generation and human decision-making, no grounding mechanisms connecting AI outputs to authoritative sources, no confidence scoring to flag uncertainty or low-reliability outputs, and assumption of accuracy rather than systematic validation.

These weren't AI failures. They were process failures. The organizations deploying AI lacked the governance and architecture necessary to make unreliable technology reliable.

Architectural Approaches to AI Reliability

Organizations getting AI right aren't waiting for perfect models. They're building verification systems around imperfect ones.

Multi-Agent Architectures with Verification

The most promising architectural pattern: multiple AI agents working in concert, with designated "verifier" agents systematically checking outputs.

Planner-Executor-Verifier Architecture:

• A planner agent formulates strategy;

• An executor carries out the task; and

• A verifier assesses output against predefined criteria before finalization.

This is particularly valuable when agents interact with external tools or when outputs are high-stakes. This does not take the accountability away from the human responsible, but with verification built-in based on proper criteria and a sequence of steps and models used, it has been helpful to reduce human verification process and the time required to achieve a high quality trusted outcome.

The verifier's role in a multi-agent system can:

• Enforce rules;

• Validate formats;

• Identify gaps in data;

• Rate credibility of sources;

• Ensure facts quoted align with research citations;

• Ensure actions are safe before they impact the real world; and

• Contain the "blast radius" of potential hallucinations.

Retrieval-Augmented Generation (RAG): Grounding in External Knowledge

RAG addresses hallucinations by grounding LLM responses in dynamically retrieved documents from internal (client specific) or external, authoritative databases. By allowing models to access current, domain-specific information at inference time, RAG makes AI systems more adaptable and verifiable.

How RAG works: RAG represents the grounding of AI agent work in indisputable facts. When a user submits a query, it's encoded into an embedding, similarity search identifies the most relevant document chunks from the knowledge base, and the LLM generates a response grounded in those retrieved documents.

Recent RAG innovations:

• Corrective RAG: Incorporates retrieval evaluators to assess document quality, adaptively handling incorrect or irrelevant information;

• Self-Reflective RAG: Enables models to dynamically decide when to retrieve information, evaluate its relevance, and critically assess their own outputs with explicit citations; and

• Information Consistent RAG: Focuses on maintaining stable and consistent outputs across semantically equivalent queries — crucial for high-stakes applications.

Citation Verification and Source Validation

The most direct approach to preventing hallucinated facts: verify every citation before it reaches users.

Automated verification systems:

• Check that links actually work;

• Score how well AI conclusions match source content; and

• Catch fabricated citations before delivery.

Confidence Scoring and Calibration

Well-calibrated confidence scores enable systems to know when they don't know.

Research shows even high-performing models like GPT-4o often display minimal variation in confidence between correct and incorrect answers, with mean differences as low as 0.4 per cent in some tests. This persistent overconfidence must be addressed through external calibration mechanisms.

Comprehensive Testing and Evaluation Frameworks

Robust testing moves beyond single metrics to multi-dimensional assessment. Evaluation platforms like LangSmith, Opik, Langfuse, and DeepEval offer observability, advanced evaluation capabilities, and real-time monitoring.

Bottom line: You're not trying to build better AI. You're building better processes around the AI you have.

The Human Governance Imperative

Architecture matters. But without governance, even the best technical systems fail. Here's why human oversight isn't optional — it's the cornerstone of reliable AI.

The Regulatory Reality: Human Oversight Is Mandatory

The EU AI Act, adopted in 2024 and progressively entering force through 2027, explicitly mandates human oversight for high-risk AI systems. Health care, finance, employment, and critical infrastructure AI must meet stringent requirements for human supervision.

The US approach emphasizes transparency, accountability, and human-in-the-loop mechanisms. Federal agencies are developing AI usage standards focusing on fairness, privacy, and national security — all requiring human judgment and oversight.

The goal: Augment human decision-making, not replace it.

Governance as Value Creation, Not Compliance Theatre

Accountability and ethics are fundamentally human responsibilities. The capacity for humans to challenge automated decisions is a critical safeguard against AI errors. Effective AI governance isn't about checking regulatory boxes. It's about creating lasting competitive advantage through trustworthy systems.

Key governance elements:

1. Define Clear Roles and Accountability: Who reviews AI outputs before they reach customers? Who monitors for drift? Who decides when to override AI recommendations? Without clear answers, responsibility diffuses and errors multiply.

2. Implement Quality Gates Throughout the AI Lifecycle: High-risk, customer-facing work requires human review. Automated checks catch formatting errors, broken citations, and policy violations. Quality gates aren't bottlenecks — they're leverage points.

3. Establish Continuous Monitoring and Audit Trails: AI systems change over time. Models drift, data distributions shift, and what worked last quarter might fail today. Audit trails are essential: logging capabilities, model versioning, and transparent decision logic support accountability and enable debugging.

4. Ensure Data Governance and Bias Mitigation: Data quality is core to AI quality control — AI systems are only as good as their training data. Organizations must implement continuous auditing and risk assessment, ethical charters, algorithm impact assessments, and bias-mitigation tools.

5. Foster Cross-Functional Collaboration: AI governance demands internal collaboration: HR, legal, IT, and Data Protection Officers must work together. This cross-functional approach ensures that regulatory requirements are interpreted correctly and that all technical aspects are adequately addressed.

The Board-Level Imperative

AI governance has become a board-level priority. Between 2023 and 2025, the percentage of S&P 500 companies disclosing AI-related risks in public filings increased dramatically, reflecting growing recognition of AI as both an opportunity and a risk requiring executive oversight.

Boards must ask:

• Do we understand the AI systems we're deploying?

• Who's accountable for their outputs?

• What processes validate reliability?

• How do we monitor for drift and degradation?

• What happens when AI gets it wrong?

• How do we manage AI risk via vendor exposure?

These aren't technical questions. They're business strategy questions about risk, trust, and competitive positioning.

A Roadmap for Quality-First AI Implementation

Most AI failures happen because organizations deploy technology before establishing the processes necessary to make it work. Here's the roadmap for planning quality ahead of time.

Phase 1: Foundation — Clean Your Data House

Before deploying AI, audit what you're feeding it. Identify all data sources your AI will access, assess data quality (accuracy, completeness, consistency, timeliness), document data lineage and provenance, establish data governance policies and access controls, and create data quality metrics and monitoring dashboards.

Why this matters: Poor data quality guarantees poor AI outputs. No amount of architectural sophistication compensates for flawed training data.

Phase 2: Architecture — Design Verification Into the System

Design AI systems with reliability as a core requirement, not an afterthought. Delineate clearly between determinate and indeterminate architecture components, implement retrieval-augmented generation for factual tasks, build verification loops, establish citation validation for any system generating references, design confidence scoring and calibration mechanisms, create fallback procedures for low-confidence scenarios, understand how to rebuild your processes with human in the centre, and implement human-in-the-loop workflows for 100 per cent confidence decisions.

Why this matters: Verification designed from the start costs less and works better than validation bolted on later.

Phase 3: Governance — Establish Clear Accountability

Define who is responsible for AI decisions and outcomes before deployment. Create AI governance committee with cross-functional representation, define roles and responsibilities for AI oversight, evaluate risk of AI assisted decision points, establish quality gates and approval workflows, document escalation procedures for AI errors or uncertainties, create incident response plans for AI failures, and develop communication protocols for stakeholders.

Why this matters: Clear governance structures ensure AI is built to serve optimal business outcomes, with appropriate checkpoints and management in place; and humans are in control of AI decision rules, processes and automations.

Phase 4: Testing — Validate Rigorously Before Deployment

Test comprehensively across multiple dimensions before users encounter your AI. Develop domain-specific evaluation datasets reflecting real use cases, test for accuracy, hallucination rates, bias, toxicity, and robustness, conduct adversarial testing (prompt injection, jailbreaks), perform edge case analysis for unusual inputs, validate outputs against authoritative sources, and run A/B comparisons against existing processes or human baselines.

Why this matters: Multi-dimensional testing is critical. This technology is new and innovative testing techniques across disciplines is what will become a source of an internal secret sauce, a competitive advantage for companies.

Phase 5: Monitoring — Watch What Happens in Production

Deployment isn't the finish line. We are dealing with indeterminate technology and an ever-evolving landscape of tools. Deployment is the start of a continuous validation and optimization process.

Implement real-time performance monitoring dashboards, alerts and halt mechanisms based on expected ranges and risk, establish Evals based on desired business outcomes, track accuracy, latency, cost, and user satisfaction metrics, monitor for model drift and data distribution changes, log all AI decisions and confidence scores for audit trails, establish thresholds that trigger human review or system alerts, and schedule regular model evaluations against updated test sets.

Why this matters: Models degrade over time. Continuous monitoring including real-time safety scoring, anomaly detection, and drift monitoring helps detect issues before they cascade.

Phase 6: Iteration — Learn and Improve Continuously

Use production data to refine systems and processes systematically. Collect user feedback on AI outputs (explicit ratings and implicit signals), track AI performance based on expected goals and outcomes, analyze failure modes and root causes, update evaluation datasets with newly discovered edge cases, refine prompts, retrieval strategies, and verification rules, retrain or fine-tune models based on real-world performance, and document lessons learned and update governance procedures.

Why this matters: The best AI systems get better over time because teams treat them as evolving capabilities, not static tools.

How Agentiiv Approaches This Challenge

At Agentiiv, we're building our platform around a fundamental principle: you can't eliminate hallucinations entirely, but you can architect systems that catch and correct them before they reach users.

Our Approach: Verification as a System Property

We're developing systems that minimize AI fabrication through carefully designed search tools requiring citations and clear instructions. More importantly, we're building a specialized link verification tool — an automated system that checks if sources are real and relevant.

Before our AI generates any report:

• It will use our verification tool to double-check every source and citation;

• The system will validate that links actually work;

• It will score how well the AI's conclusion matches source content (high, medium, or low confidence); and

• It will catch fabricated or broken citations before delivery.

This automated verification process will ensure sources exist, are credible, and actually support what the AI claims — giving you confidence scores that catch errors before you see them.

Why This Matters More Than You Think

Better, more credible base data doesn't just produce higher quality, more reliable insights — it may actually change your insight, strategy, and your whole story.

You're not just getting more accurate versions of the same conclusions. You're potentially discovering entirely different strategic directions because your AI is working with fundamentally better information.

This is the difference between AI that confirms what you already thought and AI that reveals what you didn't know.

Continuous Enhancement Through Testing

We work across several models, testing with subject matter experts, building evaluations and monitoring systems, and continuously enhancing our validation processes for our users.

This isn't a one-time engineering effort. It's an ongoing commitment to quality that reflects our latest learning on how AI systems work reliably in production.

The Organizations That Will Win

AI hallucinations aren't going away. They're mathematical properties of how current models work.

But that doesn't mean AI is unreliable — it means reliable AI requires different engineering.

The organizations succeeding with AI in 2026 share common characteristics:

• They treat AI outputs as drafts requiring verification, not finished work requiring trust;

• They architect verification into systems, not bolt it on later;

• They establish governance that makes humans accountable for AI decisions;

• They test comprehensively before deployment and monitor continuously after;

• They recognize that data quality determines output quality, period; and

• They build cross-functional teams where technical, legal, and domain expertise collaborate.

Bottom line: AI reliability is a process problem, not a technology problem.

The competitive advantage doesn't come from using better models. It comes from building better systems around whatever models exist.

The AI gold rush is creating two types of organizations: those deploying AI quickly and discovering reliability problems through failures, and those deploying AI thoughtfully and building trust through consistent performance.

The second group will dominate their markets. The first group will dominate cautionary tale case studies.

Which one will you be?