DEV Community: TARUNIKA BALAJI CSBS

Building Autonomous Multimodal AI Agents with Gemini for Real-World Reasoning

TARUNIKA BALAJI CSBS — Mon, 08 Jun 2026 15:21:28 +0000

Disclaimer

This article reflects personal research, experimentation, and architectural exploration in multimodal AI systems. The healthcare scenarios discussed are illustrative examples used to explain reasoning architectures and are not intended for clinical use, medical diagnosis, or healthcare decision-making. Any systems described should be viewed as research prototypes or conceptual implementations rather than production-ready medical tools.

Last year, I watched a medical AI system confidently misdiagnose a patient because it couldn't see what it didn't know. The system had processed X-rays, lab reports, and clinical notes—then made a decision based on the first pattern it found. No second thoughts. No recalibration. Just maximum confidence in the wrong direction.

That was the moment I stopped thinking about AI as a tool that answers questions and started thinking about it as a system that needs to reason.

This is not the story of a perfect system. It's the story of learning why most AI systems fail in the real world, and what it takes to build ones that actually think.

Why Most AI Systems Fail (And Why They Fail Silently)

Here's something they don't emphasize in tutorials: modern language models are incredibly good at sounding confident. They're trained to complete patterns, not to know what they don't know.

You ask an AI system to analyze medical images and it returns a diagnosis. But there's no:

"I'm 60% sure about this."
"The evidence contradicts itself here."
"I need more information."

There's just an answer.

I've seen this in three specific ways.

Hallucinations Aren't Bugs, They're Features

Language models generate text by predicting the next token. When they've never seen something before, they don't say "I don't know"—they often generate something that sounds plausible.

In high-stakes domains, plausible-sounding wrong can be dangerous. Overconfidence Is Structural

A single prompt, a single pass through the model, and you get certainty.

The system has no mechanism to reconsider.

Shallow Reasoning Collapses on Complexity

A patient with three comorbidities isn't just input data.

The diagnosis depends on how different pieces of information relate to each other.

The worst part?

These failures feel natural.

The Shift: From Prompting to Autonomous Reasoning Loops

Instead of asking an AI system for an answer, I started exploring systems that ask themselves questions. Research communities have been investigating chain-of-thought reasoning and agentic loops for years.

The key insight is:

Autonomy isn't about doing things without humans. It's about doing things with intention.

An autonomous system has:

A goal
Tools
State
Evaluation mechanisms
Feedback loops

It's not merely completing a prompt.

It's solving a problem.

Figure 1. Autonomous Multimodal AI Agent Architecture, Reasoning Loop, and Confidence Scoring Framework.

The architecture below represents a research-oriented prototype for reasoning across heterogeneous data sources.

Multimodal Parsing

Extracts meaning from:

Medical images
Clinical notes
Lab values
PDFs
Historical records
Reasoning Engine

The Gemini-powered reasoning loop:

Generates reasoning steps Evaluates confidence
Identifies contradictions
Determines next actions
Evidence Correlation

Tracks:

Supporting evidence
Contradictory evidence
Evidence quality
Confidence levels
Confidence Scoring

Determines whether the system should:

Conclude
Request additional information
Reconsider prior conclusions

The goal is not to maximize confidence.

The goal is to maximize justified confidence. How I Explored This Architecture

My first prototype treated reasoning as a single prompt.

def diagnose(patient_data):
evidence = parse_evidence(patient_data)
prompt = f"Analyze: {evidence}. What's the diagnosis?"
return call_gemini(prompt)

It worked.

But only superficially.

There was no mechanism for reconsideration.

So I moved toward iterative reasoning.

async def reasoning_loop(evidence, max_iterations=5):
state = ReasoningState(evidence)

for iteration in range(max_iterations):
    next_step = await gemini_reasoning(state)

    if next_step.type == "conclude":
        return next_step.conclusion

    elif next_step.type == "request_evidence":
        ...
    elif next_step.type == "reconsider":
        ...

    return state.best_conclusion()

This produced significantly richer reasoning behavior. The Reasoning Loop That Makes It Work

Figure 2. Autonomous Reasoning Loop.

The system iterates through:

Current State
Reasoning Step
Decision Point
State Update
Continue or Exit

Three possible outcomes exist:

Conclude

Evidence is sufficiently strong.

Request More Data

The confidence threshold has not been met.

Reconsider

New evidence challenges previous assumptions.

This loop is what separates reasoning systems from generation systems. Why Gemini Was a Strong Fit
Long Context

Entire patient histories can remain available without aggressive summarization.

Native Multimodality

Gemini can reason across:

Images
PDFs
Text
Structured records
Contradiction Handling

The model can explore competing hypotheses without immediately collapsing into a single conclusion.

Iterative Refinement

The system improves conclusions over multiple reasoning passes.

This is often more valuable than attempting to be correct on the first pass.

The Biggest Challenges
Challenge 1: Preventing Overconfidence

Even sophisticated models naturally gravitate toward confident outputs.

I experimented with prompts requiring explicit justification:

system_prompt = """
Include:

Confidence score
Supporting evidence
Contradicting evidence
Missing information
Alternative hypotheses """ Challenge 2: Autonomy vs Reliability

Too much autonomy creates risk.

Too much caution prevents action.

Finding the right balance remains an open research question.

Challenge 3: Evidence Fragmentation

Real-world information arrives in multiple formats:

PDFs
Clinical notes
Structured data
Natural language descriptions

Normalization becomes essential.

Challenge 4: Reasoning Consistency

The same evidence can produce different reasoning outputs.

Verification and consensus mechanisms can help improve stability.

A Failure I Still Think About

One prototype scenario looked excellent.

The system reported:

92% confidence

The reasoning appeared coherent.

But a critical piece of evidence had been misinterpreted early in the reasoning chain.

Everything afterward was built on a flawed foundation.

That experience taught me an important lesson:

Confident and wrong is worse than uncertain and honest.

This insight fundamentally changed how I approached confidence scoring.

Why Confidence Actually Matters

Figure 3. Confidence Scoring and Decision Threshold Framework.

Most AI systems:

Receive a question
Generate an answer
Present it as fact

The architecture explored here instead:

Receives a question
Reasons through evidence
Produces confidence intervals
Explains uncertainty
Identifies what could change its conclusion
Explainability Matters

When a system says:

"I'm 85% confident because of X and Y, but uncertain because of Z."

Humans can evaluate the reasoning.

When a system simply outputs:

"Answer: Pneumonia"

There is no reasoning to inspect.

Research consistently suggests that people trust systems more when uncertainty is visible and justified.

Future Research Directions

Several research questions remain open:

Confidence Calibration

How can confidence scores better reflect actual correctness?

Multi-Agent Reasoning

Can multiple specialized agents collaborate to improve reasoning quality?

Retrieval-Augmented Multimodal Systems

How should external knowledge retrieval integrate with image and text reasoning?

Human-in-the-Loop Feedback

How can expert feedback continuously improve reasoning systems?

Clinical Benchmarking

How should multimodal reasoning systems be evaluated against human experts?

These questions are likely to shape the next generation of AI reasoning systems.

Limitations

This architecture is not:

A replacement for clinicians
Perfect at multimodal reasoning
Free from training bias
Suitable for direct clinical deployment

Reasoning systems remain imperfect.

Transparency about those limitations is essential.

The Future: From Generation to Reasoning

The future of AI isn't bigger models generating longer outputs.

It's systems that:

Reason
Revise conclusions
Express uncertainty
Collaborate with humans
Learn from feedback

We're moving from:

Language Models → Reasoning Systems

That shift may ultimately prove more important than model size alone.

Closing Thought

The prototype architecture explored here isn't revolutionary.

It's simply more careful.

More skeptical.

More transparent.

More honest.

That's what intelligent systems need to be.

Not confident. Careful.

Not fast. Right.

Not impressive. Useful.

If we're going to build systems that affect health, decisions, and human well-being, honesty about uncertainty isn't optional.

It's a requirement.

About the Author

Tarunika Balaji is a CSBS student at Chennai Institute Of Technology and BS Data Science at the IIT Madras with interests in:

Multimodal AI
Agentic Systems
AI Reasoning
Healthcare AI
Human-AI Collaboration

Current areas of exploration include Gemini-powered applications, autonomous reasoning architectures, and trustworthy AI systems.

How I Built TenderReady: An AI Copilot That Helps MSMEs Win Government Tenders

TARUNIKA BALAJI CSBS — Wed, 20 May 2026 09:11:11 +0000

How I Built TenderReady: An AI Copilot That Helps MSMEs Win Government Tenders

Government tenders represent a massive business opportunity, yet millions of MSMEs struggle to participate because tender documents are long, complex, and filled with hidden eligibility criteria, compliance requirements, and legal risks.

I built TenderReady to solve this problem.

TenderReady is an AI-powered bid consultant that analyzes 100–200 page tender documents and instantly generates:

✅ Eligibility verdict (Bid / Do Not Bid)
📊 Win probability score
⚠️ Risk analysis
📋 Compliance gap report
📝 AI-generated bid response outline
💬 Tender-specific AI chat assistant

In short, TenderReady helps businesses decide in minutes whether a tender is worth pursuing.

The Problem

Government tenders are notoriously difficult to analyze manually.

Small businesses often spend days reviewing documents just to answer a few critical questions:

Am I eligible?
What documents are missing?
What are the hidden risks?
What are my chances of winning?
Should I bid at all?

Without expert consultants, many MSMEs either skip promising opportunities or waste time on tenders they cannot win.

The Solution: TenderReady

TenderReady acts like an AI bid consultant.

Users upload:

A government tender PDF
Their company profile

The platform cross-references both and generates a structured decision package.

Key Features

Eligibility Matrix

Compares tender requirements against company credentials.

Compliance Gap Report

Identifies missing certifications, documents, and qualifications.

Risk Analysis

Highlights penalty clauses, payment delays, and delivery concerns.

Win Probability Score

Estimates competitiveness based on eligibility and strategic fit.

AI Bid Assistant

Answers tender-specific questions in plain English.

Bid Response Outline

Generates a professional proposal structure with 11+ sections.

How It Works

Upload the tender document.
Enter company details.
OpenAI extracts requirements and risks.
TenderReady computes scores.
AI generates a bid recommendation and response outline.

Dashboard Preview

The dashboard presents:

Eligibility Score
Win Probability
Risk Flags
Contract Value
Submission Deadline
Document Checklist
AI Recommendations

AI Bid Assistant

Users can ask questions like:

Should I bid for this tender?
What are the biggest risks?
How can I improve eligibility?
Draft the executive summary.

Bid Response Outline

TenderReady automatically creates a structured proposal outline, including:

Executive Summary
Company Introduction
Technical Approach
Project Timeline
Quality Assurance
Pricing Strategy

Tech Stack

Built entirely using:

MeDo — Full-stack AI app builder
OpenAI API — Document analysis and content generation
Supabase — Database and secure API key storage
PDF Processing Pipeline — Tender extraction and parsing

Challenges I Faced

Eliminating Static Data

Early versions used placeholder values. I redesigned the system so outputs adapt dynamically to uploaded documents.

Handling Missing Tender Fields

Some tenders omit contract values or due dates. The UI now displays “Not Disclosed” or “TBD” gracefully.

Secure API Integration

OpenAI API keys are encrypted and stored securely via Supabase.

What Makes TenderReady Unique?

Most AI tools summarize documents.

TenderReady delivers a structured decision engine that answers the most important business question:

Should we bid, and what do we need to fix to maximize our chances of winning?

Impact

TenderReady reduces bid qualification time from several days to just minutes, empowering MSMEs to compete confidently for government contracts.

Built with MeDo

This project was developed during the Build with MeDo Hackathon and showcases how production-grade AI SaaS applications can be built rapidly with no-code tooling and advanced AI integrations.

Live Demo

https://app-bquxo6az1ce9.appmedo.com

Final Thoughts

TenderReady transforms dense tender documents into actionable business intelligence.

Upload. Analyze. Decide. Win. 🚀

If you work with government procurement, AI, or business automation, I’d love to hear your thoughts.