America’s AI Action Plan

The White House has released “Winning the Race: America’s AI Action Plan,” a 24‑page policy roadmap laying out over 90 near‑term federal actions to secure U.S. leadership in artificial intelligence. The plan focuses on three strategic pillars:

1. Accelerating AI Innovation
2. Building American AI Infrastructure
3. Leading in Global AI Diplomacy & Security

Why It Matters: This strategy signals more than rhetoric; it’s a national acceleration plan. By removing policy bottlenecks, investing in infrastructure, and promoting U.S. innovation abroad, the plan sets the stage for AI to drive economic growth, global competitiveness, and national security.

• Immediate operational impact for tech firms, energy providers, and manufacturing with streamlined permitting and procurement.
• Emerging opportunities for startups and enterprises to contribute to open-source AI and federal R&D pipelines.
• Strategic caution is warranted, as changes to governance frameworks, such as removing safeguards for DEI, climate, and misinformation, pose significant legal and reputational risks that require careful oversight.

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Use of Agentic AI for the Preparation of this Newsletter

Over the last 1 year, I have been creating content for this newsletter manually, like keeping notes of the weekly top news and any other interesting things that I feel are worth sharing. It is time-consuming, and then I spend 8-10 hours over the weekend putting everything together. Gen AI is also being used in a few areas to get assistance from Gen AI. However, all this is manual and quite fatiguing, especially when working on different browsers, searching for something interesting, reading/reviewing it, and then selecting key points to include in this newsletter.

I thought of how Gen AI can be utilized for this purpose. With this idea in mind, I set a target to create Agents to assist me from content curation to summarization, followed by human oversight review, and finally, adding it to the AI Tech Circle newsletter.

Based on this mindset, I have begun to vibe-code the entire workflow, and here is the progress so far.

The 7 AI Agents:

1 - Content Discovery Agent

• Purpose: Scrapes tech websites, RSS feeds, and social media
• What it does: Finds new tech content automatically
• Sources: manually enter the trusted sources

2 - Web Scraping Agent

• Purpose: Extracts content from web pages
• What it does: Gets full articles, metadata, and links
• Tools: Cheerio, Puppeteer for dynamic content

3 - Quality Agent

• Purpose: Evaluates content quality and relevance
• What it does: Scores articles (1-10), filters out low-quality content
• Criteria: Readability, accuracy, relevance, freshness

4 - Curation Agent

• Purpose: Selects the best content for newsletters
• What it does: Picks top articles, removes duplicates, and organizes by topic
• Output: Curated list of high-quality articles

5 - Coordination Agent

• Purpose: Orchestrates the entire workflow
• What it does: Manages task flow between agents, handles errors
• Think of it as: The "conductor" of the AI orchestra

6 - Tech News Discoverer Agent

• Purpose: Specialized in finding breaking tech news
• What it does: Monitors real-time sources for urgent updates
• Priority: High-importance, time-sensitive content

7 - Newsletter Generation Agent

• Purpose: Create the final newsletter
• What it does: Writes summaries, organizes content, formats newsletter
• Output: Complete newsletter ready for distribution

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Here is the Different dashboard:

Agent Control Dashboard

Centralized control and monitoring of all AI agents.

Key Features:

• Agent Status Monitoring: Real-time status of all agents (idle, running, error, stopped)
• Performance Metrics: CPU usage, memory usage, task completion rates
• Bulk Operations: Start, stop, or restart multiple agents simultaneously
• Content Discovery Trigger: Automated content discovery across sources
• Agent Analytics: Detailed performance analytics and health monitoring
• Error Recovery: Automatic restart of failed agents
• Agent Configuration: Individual agent settings and capabilities

Use Cases:

• Monitoring agent health and performance
• Triggering automated content discovery
• Managing agent lifecycle and operations
• Debugging agent issues

Content Pipeline Viewer

Real-time visualization and management of the content processing pipeline.

• Pipeline Visualization: Visual representation of content flow
• Status Tracking: Real-time status updates (pending, processing, approved, rejected, published)
• Content Statistics: Comprehensive analytics and metrics
• Filtering & Search: Advanced filtering by status, date, priority
• Version History: Track content changes and versions
• Approval Workflow: Streamlined approval and rejection process
• Agent Integration: See which agents are processing content
• Performance Analytics: Processing time analysis and optimization

Use Cases:

• Monitoring content processing status
• Approving or rejecting content
• Analyzing content pipeline performance
• Tracking content version history

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Content Manager

Comprehensive content creation, editing, and management interface

• Content Creation: Manual content creation with templates
• Content Enhancement: AI-powered content improvement tools
• Source Management: Integration with content sources
• Content Templates: Pre-defined templates for different content types
• Tagging System: Organize content with tags and categories
• Scheduling: Schedule content for future publication
• Workflow Integration: Seamless integration with approval workflows

Use Cases:

• Creating new newsletter content
• Enhancing existing articles
• Managing content sources and feeds
• Organizing content by categories

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Content Workflow Manager

Specialized workflow management for content-specific operations

• Content Enhancement Workflows: Automated content improvement processes
• Content Creation Workflows: Streamlined content generation pipelines
• Comparison Tools: Side-by-side content comparison and analysis
• Priority Management: Queue management with priority levels (low/medium/high)
• Batch Processing: Handle multiple content items simultaneously
• Workflow Templates: Pre-configured workflows for common content tasks

Use Cases:

• Enhancing existing content with AI
• Creating new content from templates
• Comparing different content versions
• Managing content approval workflows

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How They Work Together

1. Content Discovery → Agent Control triggers content discovery
2. Content Processing → Content Pipeline tracks processing status
3. Content Enhancement → Content Workflow Manager handles improvements
4. Workflow Orchestration → Workflow Manager coordinates all processes
5. Content Management → Content Manager provides final editing and approval

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In the coming weeks, I will keep you posted on the progress and the complete architecture. My target is to complete development in sprints and use it for this newsletter preparation.

Top Story of the Week:

Alibaba’s Qwen team introduced a massive 480B-parameter AI coding model, Qwen3-Coder-480B-A35B-Instruct, trained on 35B tokens and designed to perform high-level software development tasks across more than 90 programming languages. This marks one of the largest publicly disclosed open-source code LLMs to date.

Why it Matters: This release accelerates the open-source race in code generation and agentic development. A model of this scale enables the development of more advanced agent workflows outside U.S.-centric ecosystems, such as those offered by OpenAI or Anthropic. It also allows enterprises to explore sovereign AI coding capabilities without relying on commercial APIs, which is essential for IP control, cost efficiency, and compliance.

The Cloud: the backbone of the AI revolution

• Stargate advances with 4.5 GW partnership with Oracle, source​
• Meta is expanding its AI infrastructure and has adopted a novel approach of building weather-proof tents to house GPU clusters. This enables us to get new data centers online in months instead of years. source​

Generative AI Use Case of the Week:

Several Generative AI use cases are documented, and you can access the library of generative AI Use cases. Link​

Procurement Assistant with Bid Integrity Analytics

Use Case Description: A procurement assistant drafts scopes of work, evaluation criteria, and contract clauses with citations to the source rule or template. It also screens bids for warning signs of collusion such as bid rotation, identical pricing, and repeated text patterns. Reviewers receive a short brief, linked sources, and an audit trail for every change. The design adheres to current public guidance and pilot programs in the United Kingdom and the United States.

Business Challenges:

• A heavy drafting workload and short timelines often lead to the reuse of outdated text.
• Rules change frequently, and teams must demonstrate how every clause aligns with policy.
• Large bid volumes make it challenging to detect collusion through manual checks.
• Buyers must increase transparency about when AI is used

Expected Impact / Business Outcome:

• Revenue: Better specifications and scoring reduce delivery failures and help agencies protect value for money. OECD notes that stronger design and detection reduce losses from collusion.
• User Experience: Buyers get clean drafts with sources in minutes. Evaluators receive short bid summaries and clear risk notes. Suppliers see more precise instructions through consistent templates.
• Operations: Draft cycles shorten. Reviews become repeatable.
• Process: Mandatory clauses and transparency questions are enforced by the rules engine. Records support audits and freedom of information requests.
• Cost: Less manual drafting and earlier detection of suspect bids lower staff effort and reduce the risk of overpayment.

Required Data Sources:

• Current procurement regulations, policy notes, and model contracts.
• Template libraries and prior tenders, clarifications, and evaluation notes.
• Historic bids, awards, supplier registries, debarment lists, and price indices.
• Competition authority decisions and public case reports for training of red flag patterns.

Strategic Fit and Impact: The assistant supports national goals for the safe use of AI while enhancing productivity and trust. It supports productivity goals by freeing officials for market engagement and negotiation. It strengthens integrity by adding systematic collusion screening and complete audit trails. It also prepares organizations for new executive orders and policy updates that require contract language on AI compliance and neutrality.

Favorite Tip Of The Week:

Agents Work Better When They Talk to Each Other

Instead of building a single large AI agent to handle everything, create multiple smaller/sub-agents with distinct roles, such as researcher, planner, and executor, and let them collaborate in my above example, where I am creating several AI Agents for Content research, content filtering, etc, and then orchestrating them to work along with each other.

This “multi-agent” design enhances reliability, detects errors early, and simulates how real teams operate. It also makes it easier to test, monitor, and improve each part of your system over time. Simple idea, powerful payoff.

Potential of AI:

​Demis Hassabis, CEO of Google DeepMind, gave an interview to Lex Fridman, covering a wide range of topics, including the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini models, scaling laws, computing, and more.

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Things to Know...

Stanford HAI on Trump’s AI Action Plan

​Stanford HAI published an analysis of the Trump Administration’s AI Action Plan, highlighting its aggressive push for domestic AI infrastructure, innovation-friendly regulation, and international competitiveness. The plan focuses heavily on streamlining chip manufacturing, scaling compute, deregulating model development, and shifting federal AI funding toward commercially viable use.

Why It Matters

This marks a significant shift in U.S. AI policy from cautious governance to industrial acceleration. It favors rapid deployment, open-source development, and minimal constraints on model release, even in high-risk domains. For AI teams in regulated industries or global markets, this signals a more permissive but fragmented policy environment that could reshape how and where GenAI is built and used.

Don’t Force Agentic AI into Legacy IT

Agentic AI systems don’t fit neatly into traditional IT stacks. They require event-driven workflows, continuous memory, dynamic context management, and feedback loops, very different from classic request-response systems.

To succeed, carve out space by pilot-testing agent-based tools in parallel environments or sandboxes before attempting deep integration. Treat them as new “intelligent layers,” not simple plug-ins. This prevents operational friction and gives your team room to design new control points, interfaces, and trust boundaries

Agentic AI works best when it evolves alongside, rather than within, legacy systems.

The Opportunity...

Podcast:

• This week's Open Tech Talks episode 159 is "Mapping Your Generative AI Maturity From Aware to Transformative Part 2"

Apple | Amazon Music​

Mapping Your Generative AI M... Jul 26 · OPEN Tech Talks: Technol... 17:03   ​

Courses to attend:

• ​Retrieval Augmented Generation (RAG) Course by DeepLearning AI. This course helps you to build your first RAG system by writing retrieval and prompt augmentation functions and passing structured input into an LLM.
• ​Race to Certification 2025, from July 1 to October 31, from Oracle. Free digital training and certifications in AI, Oracle Cloud Infrastructure, Multicloud, and Oracle Data Platform

Events:

• ​GITEX Global, October 13-17, 2025, Dubai, UAE
• ​TED Conference dedicated to Artificial Intelligence, September 24-26, 2025, Vienna, Austria
• ​European Conference on Artificial Intelligence, October 25-30, 2025, Bologna, Italy.

Tech and Tools...

• ​Burn is a next-generation Deep Learning Framework
• ​LiteLLM enables you to call all LLM APIs using the OpenAI format (Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq, etc.)
• ​Sim Studio is a lightweight, user-friendly platform for building AI agent workflows.

The Investment in AI...

• ​Rune Technologies, which provides solutions for military logistics through AI-enabled predictive software, has announced $24 million in Series A funding
• ​Q.ANT received $73 million in Series A funding to advance Quantum Sensor Development and Commercialization

That’s it for this week - thanks for reading!

Reply with your thoughts or favorite section.

Found it useful? Share it with a friend or colleague to grow the AI circle.

Until next Saturday,

Kashif

The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.