Category: LLMs

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Agentforce: The New AI Wave

Last month, I attended Dreamforce 2024, the world’s largest software conference, in San Francisco. This massive annual event is always a great learning experience. Dreamforce’s 2024 key announcement was a New AI Era with Agentforce.

Agentforce is synonymous with AI Agent. As I explained in my previous blog about AI agents, I will explain Agentforce in the context of Salesforce/MuleSoft.

The study found that 90% of businesses say that their industry has become more competitive in the last three years, and 48% say it has become much more competitive. This led to decreased margins, force to more productivity, and transformed businesses to remain relevant in the market for any industry.

So the question is, how do we close this gap and become relevant to the market for any industry?

We started the AI journey with Predictive analytics as the first wave of AI. Next, we move into the Generative AI wave. Now we are next inflection point as Agentforce or AI agent. So AI Agent is waiting for us to ultimately close this gap and of course, the way that we’re going to do this is to get more time back, more productivity, and have more business growth with AI agents.

agentforce

So here are a few queries, I am trying to explain

What is Agentforce?

The newest Salesforce tool allows customers to build and customize autonomous agents to scale their workforce. It is a UX for customers to leverage with their data sources to deliver more human-like interactions.

How does Agentforce help customers achieve business goals?

Agentforce gives companies a 24/7 agent to engage on their behalf to resolve sales, service, and marketing-related.

topics including customer service cases and prospect engagement.

With Agentforce, companies can drive productivity to deliver higher profitability, while building stronger customer relationships.

How does MuleSoft enhance Agentforce?

Salesforce primarily focuses on the front end “human assistant” type of agents with the Agentforce UX, while MuleSoft primarily focuses on back-end domain expert agents who manage domain complexity (inventory, payroll.) and power other prompts or agents.

MuleSoft expands the actionability of the Agentforce agent by providing API actions and other domain assets for

broader context to the role, knowledge, actions, guardrails, and channel.

How are customers accessing data for Agentforce?

The Agentforce messaging encourages customers to use Data Cloud to bring in their data and ground Agentforce. To add MuleSoft into this conversation, leverage our value prop for MuleSoft + Data Cloud; where MuleSoft accelerates value against four use cases (on-premises, transactional, unstructured, activation):

● On-premise data: MuleSoft can run locally and stream data to Data Cloud, giving Agentforce additional context for improved grounding and better decision making.

● Transactional data: Transactional systems will want queuing, error handling, and delivery controls for ingestion

— functionality MuleSoft can easily deliver so that Agentforce agents aren’t slowed down.

● Unstructured data: MuleSoft offers pre-built accelerators for unstructured data ingestion to Google Drive,

Confluence, and SharePoint as well as OCR for images. Agentforce agents can have immediate access to data

from scanned images like government identification.

● Activation: Use MuleSoft to respond to data events in Data Cloud and drive action in real time to any downstream system for full circle updates.

What is the agent use cases that MuleSoft supports?

● Service Agents: Agentforce needs contextual data from external systems in order to deflect cases faster

● Sales Agents: MuleSoft can upload, and share leads from and with partners without compromising data integrity, securely with your governance rules. Near real-time synchronization with external systems ensures that Agentforce can engage with prospects starting at the moment leads come in.

● Commerce Agents: Setting up and managing storefronts requires data from external systems including product information, inventory levels, and pending vendor shipments. MuleSoft connects to external systems for near real-time updates so Agentforce can respond with accurate information.

● Employee Service Agents (Workday): Automating onboarding and provisioning for new hires requires data from external systems, and in some cases is unstructured data found in pdf, jpg, and png files like scanned government I.D.s and manually filled out forms. MuleSoft’s Intelligent Document Processing makes it easier to upload unstructured data so that you can share it faster with Agentforce.

How is Agentforce different from the MuleSoft AI Chain (MAC) Project?

MAC Project mainly targets a technical person, i.e. MuleSoft users and developers. With the MAC Project, customers can create powerful agents, fully composed in the MuleSoft Anypoint Platform and benefit from its End-to-End Lifecycle Governance and Management capabilities. With API Management, you can sprinkle it on top of LLM specific policies, to further implement the security aspects when interacting with LLMs. MAC Project is an open source project, which is currently being productized. Agentforce is more for non-technical users who wants to build powerful agents directly in Salesforce. It is fully integrated into every Salesforce Cloud and provides out-of-the-box integration to the Salesforce ecosystem.

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AI Agents: A New Era Of AI Integration

What are AI Agents?

An artificial intelligence (AI) agent refers to a system or program capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools. Autonomous AI agents can understand and interpret customers’ questions using natural language and translate them into business solutions.

AI journey

In recent years AI has gained a lot of momentum. Predictive analytics make the first wave of AI. Industries entered into 2nd wave of AI as generative AI. Now we are entering into 3rd wave of AI-autonomous agents. AI autonomous agents are creating a new horizon of AI implementation and AI strategy. AI autonomous agents are creating a paradigm shift that will transform how we execute our tasks and business processes daily.

How do AI agents work?

AI agents are autonomous in their decision-making process, but it require goals and environments defined by humans. Here are a few steps to define an AI agent’s goals.

  1. Data preparation and data collection — AI agents start with gathering data from all sources including customer data, transaction data, and social media. These data help to understand context and user-defined goals for AI agents.
  2. Decision-making – AI agents analyze the collected data based on machine learning models to identify patterns and decision-making.
  3. Action execution – Once a decision is made, AI agents can execute the business actions. This action includes customer queries, processing documents, executing any process, or any complex user flow.
  4. Learning and Adoption – AI agents continuously learn from each interaction, refining algorithms to improve accuracy and effectiveness. AI agents keep updating their knowledge base and enhancing their models.

How are AI agents helping organizations?

  1. Agents become building blocks that will engage with data and services on your behalf.
  2. Developers will be freed from repetitive coding tasks as AI agents get this work done.
  3. The organization will monitor and secure a network of agents in a single-agent control plane.

How AI agents will be enabling AI integration?

An AI agents provide an AI unification layer which enables your integration with AI LLMs. This feature is categorized into 3 ways.

Easy: Almost no-code development and leveraging existing skills.

Flexible: It enables you to connect multiple LLMS and switch at any time into any model. It also allows us to connect multiple databases and leverage AI innovation as they arrive.

Manageable: Deploy your AI building blocks anywhere and secure these building blocks. Easy to control from one place and reduce operating cost.

AI autonomous agents in MuleSoft

The MuleSoft Solution Engineering Team is working on an open-source AI agents project as MAC(MuleSoft AI Chain). This powerful AI agent tool can connect multiple LLMs and models to provide a unification layer for LLMs. MAC connector enables speech-to-text and text-to-speech for multiple LLMs/model providers. MAC connector leverages existing MuleSoft skills and API knowledge to integrate with any client systems. You can secure and manage this AI agent through API Manager.

Types of AI agents

Scheduled — Run in a defined window and are completely autonomous

Composed — Agents that can be triggered via APIs to be used, e.g., on a portal, as part of integrations, data assessment

Event-Driven — Agents that can be triggered on Events to service distributed applications and consumers.

Batched — Agents that process a large set of data and distribute it intelligently to multiple consumers.

Please reach out to us if you would like to know more about AI agent and integration with your systems.

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AI-Powered Experiences
Connect | Automate | Scale

Over the last few years, Generative AI has played a significant role across organizations. It is also very  interesting that just 2% expect few to no barriers to bringing Generative AI into their organization.

In IT, change is the only constant. We migrated to the cloud, we’re managing an explosion of customer data, and we’re starting to automate our processes. We expect this AI inflection point more nervous than other big waves of innovation. To manage these inflection points it is very important to streamline our AI journey.

Our first priority is to unlock our data and make it discoverable. We need to create new experiences to unlock your data, from anywhere, and to make it discoverable. This includes on-premises, hybrid/cloud data, as well as data in any format, including structured and unstructured data. Integration/APIs help you to build a framework to unlock data across all of your disparate systems.

Since data is everywhere and sources are spread across your organization. It is a human-centric task. To mitigate these human-centric tasks we need to create workflows & automate manual tasks across structured and unstructured data with minimal coding. This can be achieved by leveraging APIs, data cloud, and automation tools like RPA and IDP.

Next, we talked about the importance of building securely. With a backlog of ongoing projects, we need a way to scale the use of these API building blocks across the business, with security and governance. We need a way to protect and implement security policies across every API in your digital space before you launch your next application, like an e-commerce platform or even a mobile app. Universal API Management allows us to bring security and governance to any API.

And finally, we need just one more piece – an AI model. AI model interacts with LLMs via an API. As we make our inventory data discoverable, composable, and automated – we can build those experiences using AI models. when we bring these technologies together with an LLM, we can create intelligent AI-driven experiences. We can implement predictive and generative capabilities by using discoverable and consumable data via APIs.

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Generative AI (GenAI): Security

Generative AI (GenAI): Security

Generative artificial intelligence (generative AI) is a new buzzword across the industries. Generative AI is an artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

All organizations are investing large amounts of their budget in GenAI technology. Recently Amazon completed a $4 billion investment in generative AI development. As per a recent study barely scratching the Generative AI use case and opportunity.

Before implementing any Generative AI solution make sure you completely understand the organization’s business problem to implement Gen AI solution, because any generative AI solution takes a lot of money, time, and brain power.

Evolution of LLMs

Generative AI has just blown up within the last year or two years, but it has been around for decades. Generative AI is based on large language models (LLM).  LLM has been evolving for a while technically five to ten years approx. All companies (like AWS, Microsoft, and Open AI) are presenting their standard based on their business requirements. Here is the evolution story of LLMs & GenAI.

AI Attacks

There are four types of AI attacks.

  1. Poisoning – This AI attack can lead to the loss of reputation and capital. This is a classic example of thrill-seekers and hacktivists injecting malicious content which subsequently disrupts the retraining process.
  2. Inference – This AI attack can result in the leakage of sensitive information. This attack aims to probe the machine learning model with different input data and weigh the output.
  3. Evasion – This AI attack can harm physical safety. This type of attack is usually carried out by Hacktivists aiming to get the product of a competitive company down and has the potential to seriously harm the physical safety of people.
  4. Extraction – This AI attack can lead to insider threats or cybercriminals. Based on this the attacker can extract the original model and create a stolen model to find evasion cases and fool the original model.

Type of AI Malware

  • Black Mamba – Black Mamba utilizes a benign executable that reaches out to a high-reputation API (OpenAI) at runtime, so it can return synthesized, malicious code needed to steal an infected user’s keystrokes. It has the below properties.
    • ChatGPT Polymorphic Malware
    • Dynamically Generates Code
    • Unique Malware code
  • Deep Locker – The Deep Locker class of malware stands in stark contrast to existing evasion techniques used by malware seen in the wild. It hides its malicious payload in benign carrier applications, such as video conference software, to avoid detection by most antivirus and malware scanners. It has the below properties.
    • Targeted identification
    • Logic detonation Mechanism
    • Facial and voice recognition
  • MalGAN – Generative Adversarial Networks serve as the foundation of Malware GAN and are used to create synthetic malware samples. For Mal-GAN’s complex design to function, it is made up of three essential parts: the generator, substitute detector, and malware detection system based on machine learning. It has the below properties.
    • Generative Adversarial Malware
    • Bypass ML-based Detections
    • Feed-forward Neural Networks

AI Security Threats

  • Deepfake Attacks
  • Mapping and Stealing AI Models
  • Spear Phishing (Deep Phishing)
  • Advanced Persistent Threats (APTs)
  • DDoS and Scanning of the Internet.
  • Data poisoning AI Models
  • PassGAN and MalGAN
  • Auto Generation of Exploit code
  • Ransom Negotiation Automation
  • Social Engineering

AI Security Defense Strategy

As we learned in AI several AI malware and threats are impacting different parts of the AI ecosystem. Our AI must be smart enough that it detects its threats and mitigates risk. ML-based malware detectors detect risk and generate insights into its severity. Here are a few approaches should implement to protect your AI systems.

  • Intelligent Automation
    • Automated response and Mitigation
    • Indicators of Compromise (IOCs) extraction and correlation
    • Behavioral and anomaly detection
  • Precision Approach
    • High Accuracy and Precision
    • Identify, Understand, and Neutralize
    • Prioritize Risk
  • Define the Area for defense
    • Identify the most vulnerable area.
    • Apply a broad spectrum of defense.
    • System resiliency

AI involvement in security

  • Malware detection – AI systems help prevent phishing, malware, and other malicious activities, ensuring a high-security posture and analyzing any unusual behavior.
  • Breach risk prediction – Identify the most vulnerable system and protect against any data leak.
  • Prioritize critical defense – AI-powered risk analysis can produce incident summaries for high-fidelity alerts and automate incident responses, accelerating alert investigations.
  • Correlating attack patterns – AI models can help balance security with user experience by analyzing the risk of each login attempt and verifying users through behavioral data, simplifying access for verified users
  • Adaptive response – AI model automated response and generate an alert if the system identifies any threats. This creates the first layer of security defense.
  • Applied Machine learning – AI models are self-train. If models identify any new risk pattern apply new security models to all protected systems.