LLMs sparked trillions in generative AI market cap and AI agents are the next big thing for businesses of all sizes. AI agents automate tasks, make their own decisions, and can do things that only teams of humans do now.
Businesses like Salesforce are betting big on the rise of AI agents – e.g. Agentforce for customer success. Klarna is infamous for replacing 700 full-time human customer support agents with an AI agent – adding $40 million in profit.
While AI agents still need constant human supervision, they’re quickly moving toward becoming autonomous actors. CEO of Nvidia, Jensen Huang, also has plans to deploy up to 100 million AI Agents to help run the company. And it’s not just hype, Nividia already uses AI agents to help design chips, manage cybersecurity, and build software.
Let’s take a look at how you can use AI agents for business today and get ready for the future.
What are AI agents?
AI agents are autonomous systems designed to interact intelligently with their environment to perform specific tasks.
Unlike traditional AI tools – which often require extensive human input or monitoring – AI agents can independently analyze situations, plan actions, and execute them without constant oversight.
For example, a customer service AI agent can respond to inquiries, route complex issues to human representatives, and learn from interactions to improve its responses.
Similarly, a business process automation agent might optimize workflows by reallocating resources or predicting bottlenecks.
Unlike traditional automation tools that follow rigid rules, AI agents can learn, adapt, and handle complex, unpredictable situations.This adaptability allows them to handle tasks ranging from customer service inquiries to data analysis.
What sets AI agents apart is their ability to not only react to situations but also anticipate needs and adapt to changing circumstances—making them akin to virtual coworkers rather than mere tools.
Qualities of an AI agent from the AiEdge newsletter:
- It perceives an environment: it can receive inputs from its environment. When we think about LLMs, the inputs typically need to be in a textual or image format.
- It maintains an internal state: the internal state can be its original knowledge base with additional context that can be updated over time based on new information or experiences.
- It has goals or objectives: they influence how the agent prioritizes actions and makes decisions in varying contexts.
- It processes inputs using an LLM: not all agents are LLM-based, but as part of the agentic system, some of the agents will use LLMs as the decision engine.
- It decides on actions: based on its inputs, its internal state, and its objective, the agent will take an action. The action taken is decided by the decision engine.
- The action affects the environment: the actions taken will influence the environment either by creating new data, informing the user, or changing the internal state of other agents.
Is an LLM an AI agent?
No, LLMs are not AI Agents on their own as of December 2024.
But most AI agents use LLMs like OpenAI o1, GPT-4o or Anthropic Claude 3.5 Haiku to perform tasks, remember, and learn how to improve.
Klarna uses OpenAI-based agent to do work of 700 full-time people
Klarna, a leading global retail bank, payments, and shopping service company valued at over $6.7 billion, serves over 150 million consumers worldwide. Their journey with AI agents demonstrates how intelligent automation can transform financial services and e-commerce operations.
- The AI assistant has had 2.3 million conversations, two-thirds of Klarna’s customer service chats
- It is doing the equivalent work of 700 full-time agents
- It is on par with human agents in regard to customer satisfaction score
- It is more accurate in errand resolution, leading to a 25% drop in repeat inquiries
- Customers now resolve their errands in less than 2 mins compared to 11 mins previously
- It’s available in 23 markets, 24/7 and communicates in more than 35 languages
- It’s estimated to drive a $40 million USD in profit improvement to Klarna in 2024
How agents work together as automated teams
To get small teams of agents or hundreds of them working together, you need to understand agentic patterns.
An agentic pattern is like a team structure for a task humans perform – like a group of agents who solve problems by writing code.
The Collaboration Pattern with Three or more Agents
This is just one of the core agentic patterns you need to understand to use AI agents at scale.
10 AI Agent business use cases
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- Customer service automation – AI agents can handle first-line customer support, providing 24/7 assistance, routing complex issues to human agents, and maintaining consistent service quality across all customer touchpoints. Companies like Intercom and Zendesk are already implementing these solutions to reduce response times and support costs.
- Sales and lead qualification – AI agents can engage with potential customers, qualify leads based on predefined criteria, and schedule meetings with sales representatives. They can analyze conversation patterns and buying signals to prioritize high-value prospects and personalize outreach strategies.
- IT service desk support – AI agents can handle common IT issues like password resets, software installations, and basic troubleshooting. They can integrate with ticketing systems to manage and escalate issues, significantly reducing the workload on IT staff while providing immediate assistance to employees.
- Document processing and data extraction – AI agents can automate the extraction of relevant information from various documents like invoices, contracts, and forms. They can validate data, flag discrepancies, and input information into appropriate systems, streamlining operations in finance, legal, and administrative departments.
- Supply chain optimization – AI agents can monitor inventory levels, predict demand patterns, and automatically adjust order quantities. They can also track shipments, identify potential disruptions, and suggest alternative suppliers or routes to maintain operational efficiency.
- Compliance and risk management – AI agents can continuously monitor transactions, communications, and operations for potential compliance violations or security risks. They can flag suspicious activities, generate compliance reports, and help maintain regulatory documentation.
- Employee onboarding and training – AI agents can guide new employees through onboarding processes, provide personalized training materials, and answer common HR-related questions. This makes for consistent onboarding experiences while reducing the burden on HR teams.
- Market intelligence and competitive analysis – AI agents can continuously scan news sources, social media, and industry reports to gather competitive intelligence. They can analyze trends, track competitor activities, and provide actionable insights for strategic decision-making.
- Financial analysis and reporting – AI agents can automate the generation of financial reports, analyze market trends, and provide real-time insights for investment decisions. They can also help in fraud detection and risk assessment by analyzing patterns in financial transactions.
- Research and development support – AI agents can assist in patent searches, literature reviews, and experimental design. They can analyze research data, identify patterns, and suggest new areas of investigation, accelerating the R&D process while maintaining thoroughness.
Best AI Agents for enterprise
- Real-time code suggestions – Provides intelligent code completions directly in your IDE (e.g., Visual Studio Code, JetBrains).
- Natural language prompts – Translates plain language descriptions into functional code.
- Broad language support – Works with a wide range of programming languages, including Python, JavaScript, Java, and more.
Best For – Developers looking to speed up coding and reduce repetitive tasks.
Claude Enterprise w/Computer Use
- Computer use capability – Automates tasks like booking appointments and file management.
- Large knowledge integration – Processes and references extensive datasets.
- Team collaboration – Securely shares chats and projects.
Best For – Workflow automation and team efficiency.
- Autonomous web navigation – Extracts and organizes web data.
- Multimodal functionality – Combines text, images, and audio for task execution.
- Integration – Works seamlessly with Google tools like Gemini AI.
Best For – Market research and project management.
- Integration with 365 apps – Enhances productivity in Word, Excel, and Teams.
- Task automation – Summarizes meetings, drafts communications, and analyzes data.
- Autonomous agents – Designed for complex workflows.
Best For – Document generation and productivity.
- Customer engagement – Automates service interactions and manages sales leads.
- Industry-specific solutions – Customizable for industries like retail and finance.
- Operational efficiency – Streamlines workflows for marketing and service teams.
Best For – CRM optimization and sales automation.
- Custom AI agents – Configurable for specific business tasks like customer service, content creation, and data analysis.
- Task automation – Handles email drafting, document summarization, and data processing.
- Plugins & API integration – Access external systems, browse the web, and interact with CRM or project management tools.
- Versatility – Used across industries for customer support, knowledge management, and creative tasks.
Best For – Businesses seeking flexible, intelligent agents for diverse tasks.
- Task autonomy – Breaks down and executes tasks independently.
- Open-source platform – Customizable for unique business needs.
- Versatility – Handles content creation, data analysis, and more.
Best For – Automating creative and operational tasks.
- No-code development – Creates chatbots and virtual assistants easily.
- Customer service – Enhances interactions with natural language understanding.
- Channel integration – Works across platforms like web and social media.
Best For – Customer support and HR assistance.
How do I hire senior engineers to build AI Agents?
You could spend the next 6-18 months planning to recruit and build an AI team (if you can afford it), but you won’t be building any AI capabilities. That’s why Codingscape exists.
We can assemble a senior AI development team for you in 4-6 weeks and start building your AI Agents with the latest LLMs. It’ll be faster to get started, more cost-efficient than internal hiring, and we’ll deliver high-quality results quickly.
Zappos, Twilio, and Veho are just a few companies that trust us to build their AI capabilities.
You can schedule a time to talk with us here. No hassle, no expectations, just answers.
