Author: Shabbir Chunawalla

Google faces the charge of monopoly in search engine. It is also fighting another alleged monopolistic conduct case over technology that puts online advertising for the audience. The closing arguments will be heard in November 2024. The ruling is expected by the end of the year. If the court finds that Google engages in monopolistic conduct, there will be further hearings to explore the remedies.

The Justice department, along with some states believe Google should be forced to sell off its ad tech business. The justice department contends Google has built and maintained a monopoly in ‘open web display’ advertising. Essentially these are rectangular ads that appear on the top and right-hand side of the page when one browses the websites.

Google dominates all facets of the market — DoubleClick technology is used by news sites and other online publishers. Google Ads maintain a cache of advertisers looking to place their ads on right webpage for the targeted audience.

Google also operates AdExchange which conducts instant auctions to match the advertisers to the publishers.

Thus, it is a trifecta of monopolies which google tries to preserve, rather than serving its customers — both publishers and advertisers and winning the bids on merits.

Google brokers transactions between advertisers and publishers by extracting heavy fees, and as a result the content providers and new sites do not generate enough revenues.

In defense, Google says the government focuses only on online advertising which is a narrow niche. There is online advertising on social media, streaming TV services and app-based advertising. According to Google, it controls only 25 per cent of the market, and this share too dwindles in the face of competition.

Google also says it has invested billions in technology that facilitates the efficient match of advertisers and target audience. It says it should not be forced to share its technology and success with competitors.

The ad monopoly case is runs in Virginia and is separate from search engine monopoly case in Columbia.

During the proceedings, Google lawyer raised the issue of AmEx case related to anti-trust activity, but the judge disagreed with the relevance of that 2018 case to the present case.

In fact, the case pertains to monopolizing three separate markets for ad technology: sell-side tools used by websites ( called and servers ), ad exchanges and buy-side tools used by advertisers (called and networks). Google claims that it is wrong to put its tools in three buckets and it’s business be understood as a single market where websites publishers and advertisers transact.

AI sooner will undertake hands-on roles with the help of AI agents which are automated tools to do the specific tasks to bring an element of efficiency across industries. There is constant expansion of the usage of generative AI from year to year. The advent of AI agents in 2025 will escalate the productivity to superhuman levels. AI so far was a novelty. It will then become a necessity.

LLMs no doubt have occupied the center stage, and rightly so. Still, these have limitations. There are only incremental improvements in their functioning. Some organizations leverage a stack of technologies to maximize the benefits. Some companies use smaller models. Such combinations enhance productivity.

AI agents can be used for the least glamorous tasks. They are best suited for mundane tasks. AI can facilitate repetitive tasks leaving high judgement tasks in human hands.

One such example is the review of the legal contracts. These agents go beyond handling routine tasks. They facilitate collaboration by being active partners. They can scan unstructured documents. They can manage customer inquiries. Specialized AI agents can tackle specific tasks.

They provide conversational interfaces. They can do document analysis. They can review code and do security monitoring. They can be used in healthcare and financial services. AI can be democratized. AI teams will manage their own data. Technology will no longer serve a select few, but will be available to everyone, everywhere. Industries will be transformed — productivity will reach new heights and accessibility will be much wider.

We use software, and then we are called users. However, AI agents are the software and the software is the user. AI agents bring about a fundamental shift in the role AI plays in organisations. OpenAI’s agent called Operator is expected to be launched in January, 2025. It will serve as a personal assistant which will take multi-step action on its own. Operator will be able write code, do travel bookings and manage daily schedules. It will accomplish this by using apps already functioning on our devices and by using cloud services.

Anthropic has released Computer Use that enables Claude 3.5 Sonnet to perform complex tasks on its own.

This heralds the coming age of agentic AI.

Agentic AI understands the user’s objective and comes up with component parts to achieve the goal for the user. It plans and executes that plan. It usually uses other software and cloud services.

Three Abilities of AI Agents

1. AI agents possess the power of reasoning since at its core is an LLM that can plan and reason. It is the LLM that breaks down the problem and creates plans to solve it. It gives reasoning for each step of the process.

2. AI agents have the ability to interact with external programmes — web searches, database queries, calculators, code execution and with other AI models. The LLM decides how and when to use these tools.

3. Agents can access a memory of past events — internal logs of the agent’s thought process and history of conversations with users.

The interactions are thus personalized and context specific.

Reasoning and Acting (ReACT) are the key differences between AI chatbots and AI agents. The Acting part is really different.

AI’s future is linked to AI agents. AI agents will take over many tasks in business which are currently automated.

Agents will create new opportunities for training agentic systems.

AI agents and AI smart glasses will support each other.

AI agents have the potential to improve and degrade human capability.

Enterprise level AI could either use discriminative AI or generative AI. Discriminative AI focuses on data classification. It undertakes customer sentiment analysis, fraud detection or automation. Here classification accuracy is crucial. Discriminative AI enhances operational efficiency and decision making.

Generative AI creates new content. It also does predictive simulations. It can generate new text, image or music. It facilitates innovation and can engage customers.

While using AI, a business defines its goals. It assesses data availability. It assesses need for accuracy. It considers resource constraints. It has to be cost effective. It should have flexibility.

In November 2024, the US Justice Department and a group or states asked a federal court to force Google to sell Chrome, its popular web browser.

The request follows a landmark ruling in August 2024 by the US district court for District of Columbia which found Google had illegally maintained a monopoly in online search. The court asked the Justice Department and the states that brought anti-trust case to submit solutions to correct the search monopoly.

Apart from selling Chrome. the government asked the court to give Google a choice: either sell Android, its operating system for smartphones or bar Google from making its services mandatory on phones that use Android. If this does not work, in future, the government could force the company to sell Android.

The government asked the court to stop Google from entering into paid agreements with Apple and others to be the automatically selected search engine on smartphones and in browsers.

The government asked the court to allow the rival search engines to access Google’s data for a decade.

Since the breakup of Microsoft in 2000, these proposals are the most significant remedies requested in a tech anti-trust case. If these proposals are adopted, it will set the tone for a string of anti-trust cases against other Big Tech companies such as Apple, Amazon and Facebook.

Google’s worst nightmare would be an order to sell Chrome and Android. Chrome is the most popular browser introduced in 2008, having an estimated market share of 67 per cent of the global browser market.

Android operating system that is open source enjoys 71% of market share. It’s being open source means that mobile phone companies use it free, but then it comes bundled with Google apps already installed. The government contends that there is no level playing field.

Legal experts feel that the solution that Google should be forced to sell Chrome may not be accepted by the court, since the same break up remedy in Microsoft’s case in 2000 was overturned by an appeals court.

Google has yet to file its suggestions for fixing the search monopoly by December 2024.

Both sides can modify their suggestions before the arguments open before the court. The ruling is expected by the end of summer.

The buzzword in industry these days are the AI agents. These act as a bridge when internal data in the company is leveraged to automate complex, real-time tasks.

AI agents are powered by generative AI and large language models (LLMs). AI agents can communicate with people, solve intricate problems and respond with role-based intelligence to make informed decisions.

To begin with, generative AI enhanced work-force productivity and efficiency by performing natural language processing (NLP) tasks such as document summarization, email writing, creative brainstorming etc. AI agents are the next evolution. They are autonomous apps capable of performing multiple workflow tasks, e.g. supporting customer service, booking flights.

AI agents form an intelligent system that can understand and respond to customer enquiries without human intervention. They have the ability to automate tasks and ‘learn on the job.’ That makes business teams free to work on high-value tasks.

Agentic AI, as per Gartner, will make 15 per cent of day-to-day work decisions autonomously by 2028.

Copilot Studio of Microsoft could be used to create AI agents for routine tasks. This requires little knowledge of computer code. Microsoft uses several OpenAI models for the agents. Copilot is the user interface for AI. Salesforce has introduced Agent Force. Oracle has recently introduced over 50 AI agents. It is a significant leap forward in automating critical areas such as HR, supply chain, customer service and finance.

AI agents are set to become ubiquitous in all areas of life. It will transform business fundamentally in operations and connectivity with customers.

Big Tech is now investing in agentic AI. It has boosted the startup world. AI agents will handle more complex tasks in future, reducing the need to invest heavily in prompt engineering.

Developers would focus on high-impact work. Developers will use tools like Copilot Workplace and code scanning autofix to build faster, more secure software.

AI agents are not limited to question-answer interactions. They can handle complete workflows. It enhances decision making.

AI agents are the future of AI.

Common Business-oriented Language (COBOL) has a history of half a century plus. It is the backbone of financial and government systems. The active code lines in COBOL are estimated to 220 billion. In fact, it is a legacy programming language. It powers 95 per cent of personal banking activities.

In fact, the new survey in 2022 by Microfocus contends that COBOL footprint has spread over 775-850 billion lines of code, far exceeding the previous estimate of being between 200-300 billion lines.

There are moves to switch over to object-oriented languages such as Java. A good example is the IBM Watson Code Assistant.

AI cannot replace COBOL. It is a flawed idea. AI can write the initial bits of code (division and sections and maybe a para or two). Anything more produced by AI must be vetted by a human being since it has far-reaching implications.

COBOL persists not just because it is a legacy. It has reliability and business criticality. Most of the organizations consider COBOL apps strategic to their operations. COBOL has been integrated to cloud. The financial sector would not like it to go since it processes for them millions of transactions daily.

Organizations would like to revamp their COBOL systems, rather than replacing them. Many companies would like to opt for modernisation of COBOL system. COBOL now adapts to modern computing. It can interact with HTML, JSON, XML and even generative AI. Thus, core COBOL could be retained and new technologies can be leveraged for front-end consumer interface.

COBOL was designed for mainframe computers. These days COBOL has been made available for all the developers, e.g. GnuCOBOL.

COBOL developers earn decent salaries. However, the companies requiring COBOL code are declining. Thus, developers cannot find COBOL work in all geographies. They will have to be near the companies where COBOL is used. COBOL developers should master mainframes. At the same time, they should be in touch with other technologies.

The average salary of a COBOL developer in the USA is $84000 which could rise up to $112,000. The developer should learn the whole stack of legacy systems including COBOL, CICS, DB2, Syncsort, IBM Utilities, Changeman and JCL scripting.

Media research companies dealing with data and analytics have found acceptance in entertainment industry. Beyond media research, they can also do image consulting and audience research. These agencies must take a holistic view of the impact of various media, say social media, TV, Outdoor, You Tube etc. The impact is collective impact of the marketing effort. Media should not be isolated to measure the impact.

These media research agencies in entertain field have developed various tools for content testing, e.g. Ormax Media has developed Ormax Moviescope, Ormax Stream Test and Ormax True Value. Ormax has tested more than 1000 pieces of content by now. Still the market is untapped, since less than 10 per cent of South Indian films or shows are being tested. The number should rise to 25 per cent in the next 2-3 years.

The content should stand out in the crowd. More than 80 per cent of films and shows do not have any impact on the market. At an early stage, contest testing is done for the synopsis, or screenplay. At a later stage, it is done for post-shoot product. We gain insights on decisions such as greenlighting a script, casting, screenplay rewriting, edit changes, reshoots etc. Test research generates useful data to position the product to optimize the returns.

Cinema is an out-of-home medium. OTT is a personal consumption medium. These two can co-exist. At times, they fuel each other. Good content can fuel interest in both the mediums. At times, the product that does well in one medium may be a flop in the other medium. The audience behaviour of both the media must be studied.

In-theatre advertising is good since you get the captive audience, with high attention span and engagement.

The theatres must provide the socio-economic profile of their audiences. The exhibitors must think on these lines.

LLMs have made it possible to interact with AI systems. These systems do communicate with each other.

There are two popular frameworks to build Agentic AI applications — LangChain and LangGraph. Each has its own building blocks and differ in handling core pieces of functionality. It is for you to decide which framework best suits your purpose.

Lang Chain

It is a sequential chain of predefined commands or the use of LangChain agents. Chains are sequence of steps, and include calls to LLMs, agents, tools, external data source, procedure code etc. Chains can branch into multiple paths.

Agents or LLMs can generate responses in natural language. An agent, however, apart from using natural language, can also use capabilities to reason, call tools and repeat the process of calling tools (in case there are failures).

Tools are code-based functions. These could be called in the chain. These could also be called in the chain. These could also be invoked by an agent to interact with external systems.

Prompts include a system prompt that instructs the model to complete a task and indicates what tools are available. The user prompt instructs the model to complete.

LangGraph

It coordinates workflows like a graph. Where a linear chain or branched chain or agent system are not sufficient, LangGraph is used to handle more complex conditional logic and feedback loops.

Graphs organize workflow with flexibility. These support cyclical graphs too. Nodes represent steps in workflow. When output of one node becomes the input of another node, it is called edges. While doing so if a certain condition is met, it is called conditional edge.

State denotes the current status of application as information flows through the graph.

Agents or LLMs generate a text response to an input. Agents can make decisions about which path to take in the graph. They can perform more tasks than just text generation.

LangGraph and LangChain overlap in some capabilities. Their approach to problem is different. Lang Chain is linear and LangGraph is more granular. LangChain can be mastered easily LangGraph allows more custom control. LangChain provides an opportunely to run multiple chains or agents in parallel. LangGraph support parallel execution of nodes.

Both can be used independent of each other, or both can be used together.

Microsoft is rolling out 10 agents for its Dynamic 365 customers. These assistants will talk with prospects, confirm sales and purchase orders, reconcile invoices as per double-entry bookkeeping (invoices in ledger to cash receipts), approve expenses, resolve issues, close tickets and schedule field representatives. Many more agents will be added to this team.

Salesforce, customer relationship management organization, is introducing sales development representatives (SDRs) and Einstein Coach Agents to qualify leads before a human being interacts, schedule meetings, prepare a lead to guide the salesman in his pitch.

Intuit dominates small business accounting software. Here there will be GenOS — the coordinator, Gen SRF for security, risk and fraud and the UX or user experience. The financial agents will manager cash flows, manage emails, documents and images.

AI agents will move AI from the hallowed corporate board rooms to Main Street in 2024. Not instantly but gradually.