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Rahul Agarwal
Founder | Agentic AI... • 17h
6 key steps to build scalable Al agents. I've explained each in short, simple steps below. 1. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 → Define the agent’s core purpose → Set clear agent goals → Allocate team, tools, and data → Run ethical/privacy risk assessment early 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Support Bot → Automate 80% of Requests → 3 Devs + Zendesk Logs → GDPR & Bias Check 2. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 → Choose orchestration framework → Select a base model suitable for domain and scale → Feed domain-specific documents (FAQs, SOPs, policies) → Set safety filters and moderation rules 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: LangChain → GPT-4 → Internal Knowledge Base → Guardrails on PII & Finance Advice 3. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Design reasoning and execution logic → Integrate APIs, databases, tools → Fine-tune using domain data → Document architecture, configs, and dependencies 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Intent Router → Account API + Tools → Fine-tune on Chat Logs → YAML + Git Config 4. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 → Evaluate performance across scenarios → Test full system integration → Collect user feedback and interactions → Stress test with rare and complex cases 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Test Accuracy → System QA → Internal Feedback → Edge Case Simulations (e.g. fraud, billing fail) 5. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → Deploy to production environment → Confirm safety layers function live → Enable tracking, logging, analytics → Ensure legal/privacy compliance 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Web Launch → Filters & Guardrails On → Real-time Monitoring (Datadog) → Policy Audit Approved 6. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 → Revisit business goals → Retrain or adjust logic to reduce latency and improve UX → Continuously refine based on real-world feedback 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: New Feature Requests → Prompt Cleanup & Speed Boost → Iterate from Feedback Loop You can follow these steps to build and scale effective AI agents tailored to your company’s goals. ✅ Repost for others in your network who can benefit from this.
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