The blockchain is touted as the most significant technological innovations that have already captivated a good chunk of major industries. There has been an exponential growth in the adoption of blockchain technology in the past few years.

Yes, blockchain is a groundbreaking technology as most of the marketers state it to be, but still, it has a long way to go. We have already heard a lot about what is blockchain and how it is changing the market trends.

Now it is time to understand the significant challenges of blockchain industry.

1. Scalability:

The ability to manage a large number of users at a single time is still a challenge for the blockchain industry. Blockchain technology involves several complex algorithms to process a single transaction. As of October 2017, the total number of coinbase users is recorded to be 11.7 million. As more and more people are getting used to it, the average transactions have also increased dramatically. It severely hit the processing speed of the transactions as a higher number of people implies more computers writing and accessing the network creating an overall cumbersome system.

2. Hackers and shadow dealing:

The one thing that the blockchain industry lacks is a set of regulatory oversight making it a volatile environment and an easy target for market manipulation. For instance, the infamous one coin scam where a lot of investors lost money thinking it to be the next revolutionary digital currency was revealed to be a Ponzi scheme scam. No matter how good you are with your cryptocurrency understanding, there is always a chance that the online wallet you are using may get hacked or be blocked by the government due to some shadowy practices.

3. Complex to understand and adopt:

Blockchain technology and the complexities it involves makes it hard for a layperson to understand and comprehend its benefits. Before diving into this revolutionary application, one needs to read it through and understand the principles of encryption and distributed ledger. Another point that makes blockchain hard to adopt is that financial institutions are adequate to provide secure payment gateways and other services at affordable prices compared to the costs incurred with blockchain.

4. Privacy:

Blockchain is an open ledger which is visible for everyone to view. It is an essential aspect in many cases, but it becomes a liability if used in a sensitive environment. Blockchain technology still has to go a long way to be adopted on a broad scale. The ledger needs to be remodeled in a way that allows restricted access and is accessible only to people who are authorized to view it.

5.Costs:

Blockchain is implemented usually for eliminating the expenses related to the third parties and intermediaries involved in the process of transferring values. Though, the blockchain technology is quite beneficial it is still in the nascent stages of innovation making it tough to integrate into the legacy systems. It makes it an expensive affair overall preventing its adoption by the government as well as private firms.

6. Blockchain is still a distant dream:

The market pundits are going gaga over the blockchain technology, its benefits and how it is re-shaping the infrastructure of emerging technologies like InsurTech and others. But, the truth is that the challenges mentioned above are still hard to conquer, and it will take some good time before blockchain becomes an integral part of all the industries.

The Blockchain is an innovative technology but needs a lot of technological advancements. However, technology has an intrinsic property of evolving and can always find a way through any challenges. So, we cannot say that blockchain is going anywhere anytime soon but will take time to revolutionize the technology sector completely.

The Rise of Domain-Specific AI Agents: How Enterprises Should Prepare

By : Team Mantra

Published on Jul 20, 2025

AI AI Agent Artificial Intelligence Domain-Specific AI Agents Healthcare Insurance

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

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