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Pankaj
@javaamtho
Father|Speaker|SA(@IBM/datastax)|#EnterpriseArchitect(#TOGAF)|#ex-AWSCB|#ACE. ex-@contentserv|@InBetweenDE|@zycus it's my re/tweet/reply & my personal view !!.
Pune,MH Tham gia Haziran 2008
2.3K Đang theo dõi361 Người theo dõi
@aadit_palicha @ZeptoNow
Kindly change your 10 mins commitment to 25 mins 🙏🙏🙏👍
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How to Build a Research Agent with Multi-Step Reasoning in Langflow
A research agent goes beyond simple responses. Instead of answering directly, it plans, searches, and synthesizes information before generating an output. This workflow shows how to structure a multi-step research process using tools and reasoning.
What this flow solves
Single-step agents are limited. They often generate shallow responses and fail to explore external information properly.
With this approach, you can:
• Plan how to answer a question
• Search external sources
• Structure retrieved information
• Generate more complete and reliable outputs
Step-by-step Setup
Chat Input
Receives the initial research question.
Prompt Template (Planning)
Defines how the model should create a research plan.
Language Model
Generates a structured plan based on the user input.
Prompt Template (Plan Structuring)
Formats the previous output into a clearer research plan.
Tavily AI Search
Provides access to external information through search.
Agent
Uses the search tool to retrieve relevant information.
Prompt Template (Context Structuring)
Organizes the retrieved search results into structured context.
Prompt Template (Final Synthesis)
Defines how the final response should be generated using the query and results.
Language Model
Generates the final synthesized answer.
Chat Output
Returns the final response.
How It Works
Instead of answering immediately, the system follows multiple steps. It first plans what to search, retrieves external data using tools, organizes that information, and then generates the final answer.
This structured approach improves both reasoning and output quality.
How to get started
This template is already available inside Langflow.
Simply click New Flow, select the Research Agent template, and follow the same structure shown above.
Learn more about Langflow:
langflow.org/?utm_source=x&…
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Global Model Provider - Langflow 1.8
Configure model providers once reuse across your canvas. Configuring AI models shouldn’t require repeating API keys and provider settings across every component in a workflow.
With the launch of Langflow 1.8, model provider configuration becomes centralized and reusable:
- Providers are configured once at the platform level;
- Smart components reference a shared provider instead of raw credentials;
- Updating credentials or switching providers becomes a single change;
Example:
Rotating an API key or switching providers can now be done in one place, without reconfiguring credentials across multiple components.
Why it matters:
- Reduces setup time
- Eliminates configuration drift
- Improves security by centralizing secret management
Learn more about this release:
langflow.org/blog/langflow-…
GIF
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For weeks, a burning garbage dump in the middle of Kharadi has been filling our homes with thick black toxic smoke.
Children coughing. Families trapped indoors.
This is a health emergency.
Need immediate action please @PMCPune @CMOMaharashtra @PuneCityPolice @MirrorPune
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McKinsey studied 50 agentic AI builds and where they fail the most, and boiled it down to 6 key factors—essential for AI engineers:
1. It’s not about the agent, it’s about the workflow. don't obsess over building "impressive" agents. think about the whole system, not fun toys.
2. Agents aren’t always the answer.
Not every workflow needs a multi-agent system. Low-variance, predictable tasks are best handled with rules or ML, LLMs add complexity . The big wins for agents come in high-variance, messy processes (e.g. extract complex financial information)
3. Avoid "AI Slop". (common)
Focus on long-term development of agents, as you would with the development of an employee. Forget impressive demos. Double down on benchmarks. Agents should be given clear job descriptions, onboarded, and feedback so they improve regularly.
4. Track every step, not just outcomes.
Scaling agents up without visibility is asking for silent failures. Think about monitoring every stage of the workflow. This way teams detect errors early, refine logic quickly, and avoid total breakdowns. When mistakes happen (and they will), you can track where things went wrong and why. Don't skip this.
5. Reuse agents when you can.
Many companies waste time building one-off agents for each task. The smarter play is creating modular agent components (ingest, extract, verify, analyze) that can be reused for other workflows. Centralizing validated tools and prompts cuts 30–50% of redundant work, this number is no joke.
6. Humans remain essential, but in new roles.
Agents can parse, automate, and scale. But humans provide judgment, edge-case handling, and creative problem-solving. The future isn’t agent vs. human, but agent + human.
These are the mistakes startups and established companies make at scale. They cause massive damage to reputation and resources. And now you know how to avoid this.
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Interactive Book is Available!
I am thrilled to announce that Clean Code Cookbook is now available in an interactive format, enriched with self-evaluation quizzes!
You can sign up for a free 30-day free trial on @OReillyMedia for more than 30.000 books
learning.oreilly.com/get-learning/?…
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execed.isb.edu/content/sites/…
Strategic Leadership in a Rapidly Changing World
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@porterit_
The worst support you'll ever find. It's been 37 days and no resolution. Raised 10 complaints which were closed without my consent.
Their L1 support can't check or reopen the past tickets. The resolution team closes the ticket without solving it.
Arpit Shah@i_arpit_shah
@porterit_ The cheat company..! They have crap internal processes. It's been 20 days since I raised an initial request to transfer the money from my wallet to my bank account. More than 10 calls, but no resolution. They are closing cases without my consent.
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🚀 What is Agentic RAG? Catch these livestream highlights 👇
@rodrigonader_ walks through the agentic powers and Tool Mode in the new Langflow 1.1.
@MisbahSy demos an agent-based RAG use case, showing how an agent refines retrieval results.
🎥 Watch the replay here:
youtube.com/watch?v=Lt5FHK…
YouTube
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It’s done! 150,000 words, 200+ illustrations, 250 footnotes, and over 1200 reference links.
My editor just told me the manuscript has been sent to the printers.
- The ebook will be coming out later this week.
- Paperback copies should be available in a few weeks (hopefully before the end of the year). Preorder: amzn.to/49j1cGS
- The full manuscript is also accessible on O'Reilly platform: oreillymedia.pxf.io/c/5719111/2146…
This wouldn’t have been possible without the help of so many people who reviewed the early drafts, answered my thousands of questions, introduced me to fascinating use cases, or helped me see the beauty of overlooked techniques.
Thank you everyone for making this happen!
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Wakefit's latest campaign takes a jab at the Chief of Staff job listing, highlighting the impact of sleep deprivation. 😴💼
@WakefitCo @TheNewThing_in
#Wakefit #ChiefOfStaff #SleepDeprivation #JobSearch #MarketingCampaign #WorkLifeBalance
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