Strategies For Building Conversational AI With NLP

If you’re getting started with AI agents, this is for you 👇 I’ve seen so many builders jump straight into wiring up LangChain or CrewAI without ever understanding what actually makes an LLM act like an agent, and not just a glorified autocomplete engine. I put together a 10-phase roadmap to help you go from foundational concepts → all the way to building, deploying, and scaling multi-agent systems in production. Phase 1: Understand what “agentic AI” actually means → What makes an agent different from a chatbot → Why long-context alone isn’t enough → How tools, memory, and environment drive reasoning Phase 2: Learn the core components → LLM = brain → Memory = context (short + long term) → Tools = actuators → Environment = where the agent runs Phase 3: Prompting for agents → System vs user prompts → Role-based task prompting → Prompt chaining with state tracking → Format constraints and expected outputs Phase 4: Build your first basic agent → Start with a single-task agent → Use UI (Claude or GPT) before code → Iterate prompt → observe behavior → refine Phase 5: Add memory → Use buffers for short-term recall → Integrate vector DBs for long-term → Enable retrieval via user queries → Keep session memory dynamically updated Phase 6: Add tools and external APIs → Function calling = where things get real → Connect search, calendar, custom APIs → Handle agent I/O with guardrails → Test tool behaviors in isolation Phase 7: Build full single-agent workflows → Prompt → Memory → Tool → Response → Add error handling + fallbacks → Use LangGraph or n8n for orchestration → Log actions for replay/debugging Phase 8: Multi-agent coordination → Assign roles (planner, executor, critic) → Share context and working memory → Use A2A/TAP for agent-to-agent messaging → Test decision workflows in teams Phase 9: Deploy and monitor → Host on Replit, Vercel, Render → Monitor tokens, latency, error rates → Add API rate limits + safety rules → Setup logging, alerts, dashboards Phase 10: Join the builder ecosystem → Use Model Context Protocol (MCP) → Contribute to LangChain, CrewAI, AutoGen → Test on open evals (EvalProtocol, SWE-bench, etc.) → Share workflows, follow updates, build in public This is the same path I recommend to anyone transitioning from prompting → to building production-grade agents. Save it. Share it. And let me know what phase you’re in, or where you’re stuck. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

I've tested over 20 AI agent frameworks in the past 2 years. Building with them, breaking them, trying to make them work in real scenarios. Here's the brutal truth: 99% of them fail when real customers show up. Most are impressive in demos but struggle with actual conversations. Then I came across Parlant in the conversational AI space. And it's genuinely different. Here's what caught my attention: 1. The Engineering behind it: 40,000 lines of optimized code backed by 30,000 lines of tests. That tells you how much real-world complexity they've actually solved. 2. It works out of the box: You get a managed conversational agent in about 3 minutes that handles conversations better than most frameworks I've tried. 3. Conversation Modeling Approach: Instead of rigid flowcharts or unreliable system prompts, they use something called "Conversation Modeling." Here's how it actually works: 1. Contextual Guidelines: ↳ Every behavior is defined as a specific guideline. ↳ Condition: "Customer wants to return an item" ↳ Action: "Get order number and item name, then help them return it" 2. Controlled Tool Usage: ↳ Tools are tied to specific guidelines ↳ No random LLM decisions about when to call APIs ↳ Your tools only run when the guideline conditions are met. 3. Utterances Feature: ↳ Checks for pre-approved response templates first ↳ Uses those templates when available ↳ Automatically fills in dynamic data (like flight info or account numbers) ↳ Only falls back to generation when no template exists What I Really Like: It scales with your needs. You can add more behavioral nuance as you grow without breaking existing functionality. What's even better? It works with ALL major LLM providers - OpenAI, Gemini, Llama 3, Anthropic, and more. For anyone building conversational AI, especially in regulated industries, this approach makes sense. Your agents can now be both conversational AND compliant. AI Agent that actually does what you tell it to do. If you’re serious about building customer support agents and tired of flaky behavior, try Parlant.

We’re entering an era where AI isn’t just answering questions — it’s starting to take action. From booking meetings to writing reports to managing systems, AI agents are slowly becoming the digital coworkers of tomorrow!!!! But building an AI agent that’s actually helpful — and scalable — is a whole different challenge. That’s why I created this 10-step roadmap for building scalable AI agents (2025 Edition) — to break it down clearly and practically. Here’s what it covers and why it matters: - Start with the right model Don’t just pick the most powerful LLM. Choose one that fits your use case — stable responses, good reasoning, and support for tools and APIs. - Teach the agent how to think Should it act quickly or pause and plan? Should it break tasks into steps? These choices define how reliable your agent will be. - Write clear instructions Just like onboarding a new hire, agents need structured guidance. Define the format, tone, when to use tools, and what to do if something fails. - Give it memory AI models forget — fast. Add memory so your agent remembers what happened in past conversations, knows user preferences, and keeps improving. - Connect it to real tools Want your agent to actually do something? Plug it into tools like CRMs, databases, or email. Otherwise, it’s just chat. - Assign one clear job Vague tasks like “be helpful” lead to messy results. Clear tasks like “summarize user feedback and suggest improvements” lead to real impact. - Use agent teams Sometimes, one agent isn’t enough. Use multiple agents with different roles — one gathers info, another interprets it, another delivers output. - Monitor and improve Watch how your agent performs, gather feedback, and tweak as needed. This is how you go from a working demo to something production-ready. - Test and version everything Just like software, agents evolve. Track what works, test different versions, and always have a backup plan. - Deploy and scale smartly From APIs to autoscaling — once your agent works, make sure it can scale without breaking. Why this matters: The AI agent space is moving fast. Companies are using them to improve support, sales, internal workflows, and much more. If you work in tech, data, product, or operations — learning how to build and use agents is quickly becoming a must-have skill. This roadmap is a great place to start or to benchmark your current approach. What step are you on right now?

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

I used this guide to build 10+ AI Agents Here're my 10 actionable items: 1. Turn your agent into a note-taking machine → Dump plans, decisions, and results into state objects outside the context window → Use scratchpad files or runtime state that persists during sessions → Stop cramming everything into messages - treat state like external storage 2. Be ridiculously picky about what gets into context → Use embeddings to grab only memories that matter for current tasks → Keep simple rules files (like CLAUDE md) that always load → Filter tool descriptions with RAG so agents aren't confused by irrelevant tools 3. Build a memory system that remembers useful stuff → Create semantic, episodic, and procedural memory buckets for facts, experiences, instructions → Use knowledge graphs when embeddings fail for relationship-based retrieval → Avoid ChatGPT's mistake of pulling random location data into unrelated requests 4. Compress like your context window costs $1000 per token → Set auto-summarization at 95% context capacity with no exceptions → Trim old messages with simple heuristics: keep recent, dump middle → Post-process heavy tool outputs immediately - search results don't live forever 5. Split your agent into specialized mini-agents → Give each sub-agent one job and its own isolated context window → Hand off context with quick summaries, not full message histories → Run sub-agents in parallel when possible for isolated exploration 6. Sandbox the heavy stuff away from your LLM → Execute code in environments that isolate objects from context → Store images, files, complex data outside the context window → Only pull summary info back - full objects stay in sandbox 7. Make summarization smart, not just chronological → Train models specifically for agent context compression → Preserve critical decision points while compressing routine chatter → Use different strategies for conversations vs tool outputs 8. Prune context like you're editing a novel → Implement trained pruners that understand relevance, not just recency → Filter based on task relevance while maintaining conversational flow → Adjust pruning aggressiveness based on task complexity 9. Monitor token usage like a hawk → Track exactly where tokens burn in your agent pipeline → Set real-time alerts when context utilization hits dangerous levels → Build dashboards correlating context management with success rates 10. Test everything or admit you're just guessing → A/B test different context strategies and measure performance differences → Create evaluation frameworks testing before/after context engineering changes → Set up continuous feedback loops auto-adjusting context parameters Last but not the least, be open to new ideas and keep learning Check out 50+ AI Agent Tutorials on my profile 👋 .