Google’s Stack Builds Agents That Actually Research Like Humans

Google’s Open-Source Stack Cuts Dev Time From Weeks To Hours

Current large language models (LLMs) remain fundamentally limited by their reliance on static training data. They cannot self-identify knowledge gaps or synthesize real-time information, often leading to incomplete or outdated responses, especially on evolving or niche topics.

To overcome this, AI agents must evolve to actively recognize informational deficiencies, autonomously conduct web searches, validate findings, and iteratively refine answers – essentially functioning like a human research assistant.

Addressing this need, Google, in collaboration with Hugging Face and open-source contributors, has developed a full-stack research agent. Built with a React frontend and a FastAPI + LangGraph backend, this system integrates advanced language generation (via the Gemini 2.5 API) with intelligent control flow and dynamic web search capabilities.

The agent processes user queries, generates structured search terms, and then performs recursive "search-and-reflection" cycles using the Google Search API. It continuously evaluates results against the original query, repeating the process until it produces a validated, well-cited response.

Designed for Developers and Global Adoption

The architecture prioritizes developer-friendliness and extensibility. The frontend uses Vite + React for rapid development, while the Python-based backend leverages FastAPI and LangGraph for complex decision-making, evaluation loops, and autonomous query refinement.

Clear code separation (e.g., agent logic in graph.py, UI components in /frontend) allows easy modification. Setup is straightforward, requiring Node.js, Python 3.8+, a Gemini API key, and a simple make dev command, with endpoints running locally on ports 2024 (backend API) and 5173 (frontend UI). This design makes the system accessible for global research teams and developers.

Key Innovations and Practical Impact

The agent's core innovation lies in its autonomous reasoning:

• Reflective Looping: It evaluates search results, identifies coverage gaps, and refines queries without human input.

• Delayed Synthesis: It waits until sufficient information is gathered before generating a response.

• Built-in Citations: Answers include embedded hyperlinks to sources, ensuring traceability and trust.

This capability makes it ideal for academic research, enterprise knowledge bases, technical support, and consulting – anywhere accuracy and validation are critical. It represents a significant shift from stateless Q&A bots towards real-time reasoning agents capable of genuine investigation and verification.

By combining Gemini 2.5's language capabilities with LangGraph's logic orchestration, this project delivers a breakthrough in autonomous AI research. It demonstrates how complex research workflows can be automated while maintaining high standards of accuracy and transparency. As conversational AI evolves, systems like this establish the benchmark for intelligent, trustworthy, and developer-accessible research tools.

Adobe Introduces AI-Powered Agents for Smarter Marketing Workflows

Adobe has introduced two new AI agents designed to optimize marketing workflows by helping businesses build, manage, and coordinate AI tools across Adobe and third-party platforms.

1. Product Support Agent:

• Provides interactive troubleshooting and issue diagnosis.

• Automatically gathers contextual data (logs, metadata, user sessions) to pre-fill support cases.

• Allows user review before submission and offers real-time ticket status updates.

• Addresses operational roadblocks that disrupt workflows by reducing time spent on manual ticket management and technical documentation searches.

2. Data Insights Agent:

• The first agent was built on Adobe's Experience Platform Agent Orchestrator.

• Enables users to query data using natural language.

• Automatically builds visualizations within Adobe Customer Journey Analysis (Analysis Workspace).

• Significantly shortens the time to discover and deliver actionable insights.

Lokesh Alluri (Digital & Marketing Analytics Manager, Lenovo) emphasized the Data Insights Agent will be transformative, stating: "By streamlining time-intensive workflows, from reporting to forecasting, we can ensure every stakeholder has timely data to drive initiatives that enhance customer satisfaction."

These agents aim to reduce manual, time-consuming tasks, particularly cross-functional troubleshooting and complex data analysis, allowing teams to focus on core responsibilities and strategic initiatives.

RSM Commits $1 Billion to AI Agents and Digital Services Expansion

RSM US, the largest U.S. accounting firm outside the Big Four, plans to invest $1 billion over the next three years to scale its use of artificial intelligence, significantly surpassing its previous AI spending.

The investment will be directed toward integrating generative AI into internal operations, client-facing platforms, and supporting middle-market companies with their AI adoption strategies, according to Sergio de la Fe, RSM US enterprise digital leader and partner.

The funding will also support the development of AI infrastructure, predictive models, talent upskilling, and partnerships to strengthen RSM’s AI capabilities. Previously, RSM allocated $150–$200 million to AI over three years, but de la Fe said the firm now aims to standardize and industrialize its AI use.

Initially, generative AI tools were given to individual employees, but inconsistent usage and prompting limited their impact. The firm has since built automated, multi-step workflows powered by AI agents—systems capable of taking action or making decisions autonomously, resulting in productivity gains of up to 80%, compared to the earlier 5–25% range.

One example is RSM Atlas, an AI-driven compliance tool. Previously, employees manually reviewed each regulation to assess client compliance, sometimes aided by generative AI. Now, the entire process is automated, significantly accelerating review speed. In audits, where professionals once manually checked 100-page disclosures, AI now performs the task faster and with improved accuracy.

RSM reported around $4 billion in U.S. revenue for the fiscal year ending April, flat year-over-year, while global revenue reached $10 billion in 2023, a 6% increase.

The firm is currently merging with its U.K. counterpart, a move that will create a combined 23,000-person workforce spanning the U.S., U.K., Canada, Ireland, India, and El Salvador. The deal is expected to close by the end of the year. The U.K. arm is also making its own AI investment, though exact figures haven't been disclosed.

RSM’s commitment follows BDO USA’s recent $1 billion AI announcement and similar moves by Big Four firms. For instance, PwC committed $1 billion in 2023 toward U.S. generative AI integration, which enabled the launch of AI assurance services—third-party verifications that AI systems are performing as intended.

Although RSM is not currently including such attestations in its investment plans, its audit teams are evaluating how AI assurance could eventually serve middle-market clients. “The middle market has a bit more time before it needs to fully address that,” said de la Fe.

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