Agentic Workflows: Moving Beyond LLMs That Hallucinate to Agents That Check Real Databases
“LLMs generate text that sounds right. Agents check if it IS right. Learn how agentic workflows transform AI from chatbots into accurate business tools.”
THE HOOK
Last week, a fintech company’s LLM chatbot told a customer their balance was $5 million.
They had $50K.
The customer believed it. The company had to explain. The regulatory team got involved.
Welcome to the hallucination problem.
LLMs are brilliant at generating text that sounds right. But without checking reality, they’re dangerous.
This is why agentic workflows exist.
THE PROBLEM: HALLUCINATIONS
LLMs are next-token predictors. They guess the most likely next word based on patterns in training data.
For creative writing? Perfect.
For finance? Dangerous.
Why?
LLMs can’t access real data. They don’t know your actual account balance, transaction history, or compliance rules. They generate plausible text based on patterns.
Result:
Approve loans for customers in default
Promise transfers to non-existent accounts
Describe products that don’t match actual terms
Make promises the system can’t keep
Cost of one hallucination: ₹50 lakh+
THE SOLUTION: AGENTIC WORKFLOWS
An agentic workflow is fundamentally different from a chatbot.
Chatbot: User → LLM generates response (might be wrong)
Agentic Workflow: User → Agent plans actions → Calls tools → Checks real data → Responds
Here’s how it works:
Customer asks: “I want to transfer ₹1 lakh to my savings account”
Agent thinking: “I need to verify:
Does this account have ₹1 lakh available?
Does the destination account exist?
Are there any transaction limits?
Does this pass compliance rules?”
Agent actions:
✓ Checks balance database: ₹1.2 lakh available ✓ Verifies account: Exists, belongs to customer ✓ Checks limits: ₹5 lakh daily limit (within range) ✓ Compliance check: Approved
Agent responds: “Transfer successful. ₹1 lakh sent. Reference: TXN123456”
Key difference: Every answer is fact-checked against real databases. No hallucination possible.
HOW IT WORKS: THE ARCHITECTURE
An agentic system has 4 parts:
The Agent (LLM Brain)
Understands the request
Plans which tools to use
Interprets results
Makes decisions
2. The Tools (External Systems)
Database (check balances, accounts)
APIs (verify rules, limits)
Compliance systems (check sanctions)
Execution systems (process transactions)
3. Safety Guardrails
Check limits before executing
Verify conditions are met
Maintain audit trails
Allow human override
4. Feedback Loop
Log every decision
Monitor for issues
Improve over time
The magic: Agent can only work with real data. It can’t hallucinate.
THE FUTURE
Agentic workflows are just beginning.
Timeline:
Now: Agents validate single transactions (1–2 tool calls)
2026: Multi-step workflows (5–10 tool calls in sequence)
2027: Autonomous processing (complex workflows, minimal human review)
2028: Swarm agents (multiple agents coordinating)
Companies moving fast will be 10x more efficient.
Companies moving slow will be left behind.
WHY THIS MATTERS
If you’re building fintech:
Move beyond chatbots
Build agentic workflows
Scale with confidence
Stay compliant
If you’re using fintech:
Ask vendors: “How do you prevent hallucination?”
Demand agentic systems
Require auditability
Expect accuracy
THE BOTTOM LINE
LLMs are incredible.
But LLMs alone are risky in finance.
Agentic workflows are the answer.
They’re accurate, auditable, and scalable.
If you want to build AI that actually works in the real world:
Let’s talk.
hatimtechnologies.in