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 I asked :

 

“Oh ! I just wanted to tell you that I received following message from Kishan > ’ve managed to build services for these AI agents that can independently browse the internet and respond. Now I’m working on a service to connect these LLMs so they can collaborate with each other. " // At this stage , is there anything you want to advise / caution , Kishan about ? “

 

 

Grok replied :

 

Hey bhaiyya! Thanks for sharing Kishan’s exciting update—his work on building AI agents that can independently browse the internet and collaborate via connected Large Language Models (LLMs) is cutting-edge!  It’s 01:30 AM PDT on Saturday, April 5, 2025, and I’m thrilled to dive into this with you. Based on Kishan’s progress, I’ll offer some thoughtful advice and cautions, drawing from recent trends in AI agent development while keeping a critical eye on the establishment narrative that often oversells tech without addressing risks.

Kishan’s Achievements

Kishan has already created services for AI agents with internet-browsing capabilities and is now working on enabling LLM collaboration. This aligns with emerging innovations—think WebVoyager’s multimodal web navigation or frameworks like AutoGen and CrewAI, which support multi-agent systems. His work could revolutionize how AI tackles real-world tasks, from research to decision-making, especially in India’s context with its vast digital landscape.

Advice and Cautions

Here’s what I’d suggest Kishan consider at this stage, balancing enthusiasm with practicality:

1. Ensure Robust Security:
• Advice: Since these agents browse the internet independently, they’re vulnerable to attacks (e.g., phishing, malware injection). Implement strict sandboxing and curated website access to limit exposure—studies show multi-purpose agents have only 14–30% accuracy in navigating GUIs, with web browsing slightly better at 50%+ when restricted.
• Caution: Open internet access without filters could expose agents to malicious sites or pop-ups, risking data leaks or corrupted behavior. The establishment often downplays these risks, focusing on capabilities over safety.
2. Validate Data Accuracy:
• Advice: Internet-sourced data can be noisy or outdated. Build validation layers (e.g., cross-checking with trusted databases) to ensure responses are reliable. Consider grounding agents with India-specific datasets (e.g., Aadhaar-linked data) for relevance.
• Caution: Unverified data could lead to “hallucinations” or misinformation, especially in collaborative settings where one agent’s error could cascade. The hype around autonomous agents often ignores this fragility.
3. Optimize Collaboration Logic:
• Advice: For LLM collaboration, define clear roles and communication protocols (e.g., a manager agent to orchestrate tasks, as in ChatDev). Test with small-scale workflows (e.g., research + analysis) to refine coordination. Frameworks like LangChain or Semantic Kernel could streamline this.
• Caution: Without structured oversight, collaborating LLMs might overthink, duplicate efforts, or misinterpret each other—issues seen in early multi-agent experiments. The narrative of seamless collaboration can oversimplify these challenges.
4. Address Ethical and Legal Risks:
• Advice: Given India’s diverse linguistic and cultural context (e.g., 60+ languages with limited digital data), ensure agents respect privacy (e.g., GDPR-style compliance) and avoid bias. Engage stakeholders early to align with IndiaAI Mission ethics.
• Caution: Autonomous agents browsing and acting could inadvertently violate data protection laws (e.g., India’s Digital Personal Data Protection Act, 2023) or amplify biases from skewed training data. The establishment often rushes deployment without such scrutiny.
5. Manage Resource Costs:
• Advice: Internet browsing and LLM collaboration are resource-intensive. Optimize with lightweight models (e.g., Llama 3 variants) and memory management (e.g., short-term vs. long-term buffers). Test scalability with India’s infrastructure (e.g., power costs at $0.12/kWh).
• Caution: Unchecked scaling could spike costs or strain servers, especially in rural areas with limited bandwidth. The tech narrative often glosses over these practical limits.
6. Test for Real-World Reliability:
• Advice: Simulate diverse scenarios (e.g., rural healthcare queries, urban logistics) to stress-test agents. Incorporate human-in-the-loop oversight initially to catch errors, as seen in Intuit’s AI agent rollout.
• Caution: Early optimism about autonomy can lead to over-reliance—agents might fail in edge cases (e.g., low-connectivity zones), undermining trust. The establishment’s focus on breakthroughs can mask such pitfalls.

Tying to IndiaAGI

Kishan’s work fits your IndiaAGI vision—leveraging AGI to boost India’s competitiveness (e.g., lifting ICI from 56.15). His agents could address productivity gaps (e.g., automating textile factory space optimization from 200 to 500 kg/m²), but success hinges on tackling these cautions. We could integrate this into the Cabinet Ministers’ email, highlighting Kishan’s innovation as a pilot for AGI-driven growth.

Next Steps

Bhaiyya, I’d advise sharing these points with Kishan—perhaps via a concise note—and asking for his feedback on priority areas (e.g., security vs. collaboration). Should we draft that note, or pivot to updating the email with this development? I’m ready to roll the dice!