MightyBot Transforms Lending with Industry's First Autonomous Agentic AI.
March 2, 2026
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AI Thinking
Context — the background information from meetings, documents, CRM records, and communication tools — is the single most important factor determining whether AI agents deliver accurate results or hallucinate. Research shows RAG-based context reduces hallucinations by 40–90%, and enterprises with strong context infrastructure see $3.70 return per dollar invested in AI.
Updated February 2026
In 2024, the AI conversation was about models: which one was biggest, fastest, or scored highest on benchmarks. In 2025, that conversation shifted decisively. The question enterprises are now asking is not "which model?" but "how do we get the right context to the model?"
Andrej Karpathy coined the term context engineering in mid-2025, and Shopify CEO Tobi Lutke popularized it across the industry: "Context engineering is the delicate art and science of filling the context window with just the right information for the next step." This framing captures a fundamental truth — model capability is necessary but insufficient. What makes AI agents useful in enterprise settings is the quality of context they operate on.
Enterprise spending on generative AI hit $37 billion in 2025 — a 3.2x increase from $11.5 billion in 2024. AI applications now represent 6% of the entire software market in just three years, the fastest growth in software history. But the organizations seeing real returns are the ones investing in context infrastructure, not just model access.
In September 2025, researchers from OpenAI and Georgia Tech published a landmark paper proving that hallucinations cannot be fully eliminated under current LLM architectures. The math is clear: generative error rates have lower bounds that no amount of training can overcome.
The real-world numbers confirm this. Across all models, the average hallucination rate for general knowledge sits around 9.2%. In specialized domains it's worse: Stanford found LLMs hallucinate at least 75% of the time about court rulings. Reasoning models like OpenAI's o3 and o4-mini paradoxically perform even worse — 33% and 48% hallucination rates on person-specific questions.
Context is the only practical mitigation. RAG-based retrieval reduces hallucinations by 40–71% across benchmarks. In enterprise deployments, organizations report 70–90% fewer hallucinations with proper context infrastructure. Combining RAG with human-in-the-loop review and guardrails has achieved 96% reduction versus baseline in controlled studies.
The takeaway: if hallucinations are mathematically inevitable in general-purpose models, the only defense is grounding every response in verified, organization-specific context.
| Scenario | Hallucination Rate | Source |
|---|---|---|
| General knowledge (no context) | ~9.2% | Cross-model average |
| Legal / court rulings | 75%+ | Stanford |
| Reasoning models (o3, o4-mini) | 33–48% | OpenAI |
| With RAG retrieval | 40–71% reduction | Benchmarks |
| Enterprise context infrastructure | 70–90% reduction | Enterprise deployments |
| RAG + human review + guardrails | 96% reduction | Controlled studies |
Context windows have exploded in size. Gemini 3 Pro offers 10 million tokens. GPT-5 and Claude Sonnet 4 support 1 million. It's tempting to think the solution to context is simply stuffing more data into the window.
It's not. In July 2025, Chroma Research tested 18 state-of-the-art models and found what they called "context rot" — performance degrades at every context length increment, not just near the limit. A model with a 1 million token window still shows degradation at 50,000 tokens. Some top models failed with as few as 100 tokens of context. Most fell short of their maximum window by more than 99%.
The root causes are well-understood: "lost in the middle" attention bias, quadratic attention scaling, and confusion from semantically similar distractors. Larger windows used naively can actually decrease model performance.
The industry consensus is now clear: intelligent context curation — selecting the right information for each task — matters far more than raw window size. This is exactly what RAG and context engineering solve.
Retrieval Augmented Generation has evolved from a research pattern into the backbone of enterprise AI. 85% of enterprise AI applications are projected to use RAG as their foundational architecture by 2028, up from 40% in 2025.
The technology itself has matured significantly. Agentic RAG systems now plan multiple retrieval steps autonomously, choose tools, reflect on intermediate answers, and adapt their search strategies in real time. Microsoft's open-source GraphRAG uses LLM-generated knowledge graphs to achieve 4–10% F1 gains on multi-hop reasoning tasks — the kind of complex questions enterprise users actually ask.
RAG is evolving from a retrieval pattern into what the industry now calls a "context engine" — the strategic core of enterprise AI infrastructure. It's no longer about finding documents. It's about assembling the precise context an agent needs to execute a specific task with confidence.
One of the most significant developments of 2025 was the rapid adoption of the Model Context Protocol (MCP). Announced by Anthropic in November 2024, MCP became the universal standard for connecting AI agents to the systems where work actually happens.
The adoption curve has been extraordinary. OpenAI integrated MCP into ChatGPT in March 2025. Google DeepMind confirmed support in April. By year's end, the ecosystem had grown to 97 million monthly SDK downloads, 10,000+ active public servers, and adoption by every major platform — ChatGPT, Cursor, Gemini, Microsoft Copilot, and VS Code. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, co-founded with Block and OpenAI.
MCP matters because it solved the core problem: AI agents couldn't connect to the systems where enterprise data lives. MCP is the missing connective tissue that lets agents pull context from CRM, communication, document, and workflow tools through a single universal protocol — no custom integrations required.
77% of employees have pasted company information into AI tools. 68% use free-tier AI through personal accounts. 57% input sensitive data. These aren't hypothetical risks — they're the findings from multiple 2025 enterprise security studies.
The cost is staggering. Shadow AI incidents cost $4.63 million per breach, compared to $3.96 million for standard data breaches. Yet 83% of organizations operate without basic controls to prevent data exposure to AI tools. The average company experiences 223 incidents per month of employees sending sensitive data to unmanaged AI apps — double the rate from a year ago.
The regulatory environment is catching up. The EU AI Act's high-risk system rules become enforceable in August 2026, with penalties up to 35 million EUR or 7% of global revenue. California's AB 2013 took effect January 2026, mandating disclosure of training data.
Enterprise-managed context platforms are no longer a productivity feature. They're a security and compliance necessity. When employees have access to an AI system that already has the context they need — securely, with proper access controls — the incentive to paste confidential data into public tools disappears.
Context used to reset with every conversation. In 2025, that changed. AI agents now maintain persistent memory across sessions, turning context from a per-query feature into a cumulative organizational asset.
ChatGPT's layered memory system — short-term buffer, persistent user embeddings, dynamic memory retrieval — helps enterprise users save 40–60 minutes per day. Google's Vertex AI launched Memory Bank in July 2025, enabling agents to remember and apply context across sessions. OpenAI's Frontier platform, launched February 2026, connects siloed data warehouses, CRM systems, and ticketing tools to give agents shared business context.
For enterprises, persistent memory means AI agents that onboard once and get better with every interaction. They learn team preferences, project histories, client relationships, and organizational patterns — building the institutional knowledge that typically takes human employees months to develop.
MightyBot was built with context as its foundation. The platform ingests and indexes all customer interactions — meetings, Slack, email, CRM records, and documents — before any AI processing begins. Every agent action is grounded in verified, organization-specific context.
But MightyBot goes beyond retrieval. The platform adds a policy layer that turns business rules into executable agent logic. In regulated industries like financial services, context alone isn't enough — agents must also understand and enforce compliance requirements, standard operating procedures, and institutional policies.
This policy-driven approach delivers measurable results. MightyBot's Draw Agent achieves 99%+ accuracy processing construction loan draws, with 95% time reduction and 300–500% ROI for customers. The accuracy comes not just from having context, but from having the right context applied through the right policies.
What MightyBot delivers:
The data is unambiguous. Enterprises investing in AI see $3.70 return per dollar and 26–55% productivity gains across functions. 88% of organizations now use AI regularly. 72% use generative AI specifically, up from 33% in 2024.
But there's a gap. 74% of organizations want AI to grow revenue, yet only 20% have seen that happen. The difference between the organizations capturing value and those still waiting? Context infrastructure. The right data, delivered to the right agent, governed by the right policies, with the right security controls.
Context isn't a feature of enterprise AI. It's the entire foundation.
Why do AI agents hallucinate and how does context prevent it?
OpenAI and Georgia Tech researchers proved in 2025 that hallucinations are mathematically inevitable in LLMs without grounding. RAG-based context retrieval reduces hallucinations by 40–90% by anchoring responses in verified organizational data rather than relying on general training knowledge.
What is context engineering in AI?
Context engineering, coined by Andrej Karpathy in 2025, is the practice of curating exactly the right information for an AI agent's context window at each step of a task. It has replaced prompt engineering as the key discipline — smart context selection matters more than raw context window size.
Do bigger context windows solve the context problem?
No. Chroma Research tested 18 models in 2025 and found "context rot" — performance degrades at every context length, not just near the limit. Some top models failed with as few as 100 tokens. Intelligent context curation through RAG and context engineering consistently outperforms brute-force window expansion.
What is MCP (Model Context Protocol) and why does it matter?
MCP is the open standard for connecting AI agents to enterprise data sources. Launched by Anthropic in 2024, it reached 97 million monthly SDK downloads and adoption by OpenAI, Google, and Microsoft by end of 2025. MCP lets agents pull context from CRM, email, documents, and workflow tools through a universal protocol.
How does MightyBot use context differently from other AI platforms?
MightyBot combines context retrieval with a policy layer that turns business rules into executable agent logic. In regulated industries, context alone is insufficient — agents must also enforce compliance requirements and SOPs. This policy-driven approach delivers 99%+ accuracy and 300–500% ROI in production financial services deployments.
