Summarizer Pro

From Curiosity to Clarity—Research, Refine, Deliver.
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devchallenge
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agentaichallenge
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ai
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machinelearning
This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

What I Built
I developed an Autonomous Research AI agent that automates end-to-end research, analysis, and insight generation. Frustrated by the time-consuming process of manually scouring the web, synthesizing data, and refining results, I built this agent to act as a "smart research assistant" who thinks while it works. Users submit a topic and areas of interest, and the agent:

Researches the topic across trusted web sources,
Generates a draft summary,
Critically reflects on its own output to identify gaps or biases,
Iteratively improve the summary into a polished, actionable answer.
Agent Flow [1]
Agent flow

Technical Details:
Control Flows: The agent uses advanced control flows for orchestration, ensuring seamless transitions between research, summarization, and reflection phases.
Multi model: uses different models for different tasks
Claude for prompt writing: uses Claude excellent prompt writing capability to prompt perplexity
Google LLM with Large Context Window: For summarization, the agent leverages Google’s LLM, which excels at handling large volumes of data while preserving context and coherence.
GPT-4o for Reflection: GPT-4o powers the agent’s self-reflection phase, identifying knowledge gaps, biases, and areas for improvement to refine the summary iteratively.
Perplexity for web search: Fetches facts and data form web using perplexity
Modular Design: The agent is built with modularity in mind, allowing easy integration of additional tools or APIs for enhanced functionality.
Why?
Traditional search tools overwhelm users with raw data. This agent tackles that by delivering refined, context-aware insights—perfect for time-constrained professionals, researchers, or even other AI agents needing preprocessed data.

Envisioned Use Cases:

Accelerating due diligence for startups.
Generating unbiased summaries for topics of interest
Powering real-time market trend reports.
Demo
Link to agent: https://agent.ai/profile/researcher-pro

Scenario: A user asks, “How deepseek trained their r1 model?”

Link to run: https://agent.ai/agent/researcher-pro?rid=361127f4c4774eabb37f5cfbbe220fa0

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Agent.ai Experience
Delightful Moments:
Rapid Prototyping: The platform’s intuitive interface let me spin up a functional agent in under an hour.
Debugging Made Simple: Real-time logs and error tracing helped me quickly identify bottlenecks in the research-refinement loop.
Out-of-the-Box Utilities: Pre-built tools like web data fetchers and source validators eliminated grunt work, letting me focus on core logic.
Challenges:
Caching Complexity: I struggled to implement a caching layer to avoid redundant web fetches.
Preview Quirks: The agent preview pane in Brave browser occasionally froze after code updates, forcing manual restarts. A smoother refresh workflow would save frustration.
References:
[1] https://github.com/langchain-ai/ollama-deep-researcher