Agentic Frameworks: The Backbone of Autonomous AI Systems for Modern Businesses

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There’s a lot of buzz around AI agents and how the autonomy potential of agentic systems is set to transform businesses ‘work as we know it. Now, while AI evangelists herald a new age with AI’s role in human society coming full circle (tool-advisor-overseer-overlord), doomsayers bemoan the loss of human agency and stoke takeover and unemployment fears. What’s often lost in the hype is an objective appreciation of Agentic frameworks—the architecture determining how AI agents perceive and interact with their environment to achieve complex goals.

Here we take an in-depth look at agent frameworks, what they bring, what makes them tick, and most importantly, what goes into successfully building and deploying agentic systems.

Understanding AI Agentic Frameworks

Here’s an interesting analogy for agent frameworks – skeletons. Consider how our bones and joints determine our range of motion and physical capabilities. Skeletal structures differ wildly across unlikely relatives such as elephants and small rodent-like rock hyraxes, showing how frameworks can diverge, evolving to uniquely fit a particular niche.

AI Agentic Frameworks are the skeleton to your AI agent/s (brain), letting LLMs take actions, carry out tasks, make decisions, and interact with other systems to achieve complex goals. They are software systems that enable LLMs to move from passive content generation or query answering towards decision-making, the way we would. It’s this ability to not just present solutions but implement them, and make real-time decisions based on changing data and user inputs, while learning from their environment, that excites businesses.

How Agentic Frameworks Enhance LLM Involvement in Business

LLM models are designed to process data at scale, understand human language, and generate natural, conversational responses. Inhibiting them is the deliberate social distancing from external systems in real-time, limiting them to a never-ending pop quiz.

Here’s where agentic frameworks come in, bringing agency to LLMs and giving developers a faster, simpler way of building agentic systems.

What AI Agentic Frameworks bring to the table:

  • Agent Abstractions provide a common way to build, simplifying onboarding and project maintenance. These make frameworks useful across applications (easier to get started), but make it harder to see what’s going on or control it.
  • Short-term memory storage, which enables multi-turn experiences such as chats.
  • Long-term memory for cross-thread memory, which allows agents to learn from past experiences.
  • Human Control: Most agentic systems need or benefit from a human-in-the-loop. Frameworks allow getting user feed. Control includes interruptibility, oversight hooks, or manual tool approval phases.
  • Debugging/Oversight becomes simple by letting you inspect the exact steps taken, and inputs/outputs at each step.
  • Fault Tolerance is vital for building distributed applications, enabled by robust workflows and programmable retries.

A conventional e-commerce chatbot merely answers questions or makes recommendations, but an AI agent could also process and execute requests like changing an account password or issuing a refund. What’s more, it could check real-time data, events, and even local weather patterns to make forward-thinking suggestions, while learning from a user’s past purchases and decisions.

Agentic Workflow Design for Maximum Impact

The most common agentic problems, rather than being LLM fit or quality related, come from incorrect contexts being passed to the model. And the agentic workflow controls that, the MVP among agentic system components. Workflows are a distinct pattern where the flow is deterministic, allowing less control to the LLM. For all the autonomy talk, most real-world agentic systems are a combination of workflows and agents. Why’s that?

For high-leverage decision making, priority numero uno is ensuring the LLM has the right context. Workflows are deterministic, letting you control exactly where the data flows. They make passing the right context to the LLM easy. That’s why effective agentic workflow design tailored to your overall purpose is key. Clearly define the individual tasks you want AI-automated, where the needed data at every node is sourced from, which tools are called, and where the results flow.

Consider these factors while designing your Agentic Workflow:

  1. Task Granularity: Define the level of complexity for each task assigned to the AI, from simply database querying to complex supply chain management.
  2. Interoperability: Ensure the framework integrates seamlessly with your existing tools and software: CRM system, marketing platform, enterprise resource planning (ERP) system, etc.
  3. Error Handling: Determine how your AI agent handles failures – escalate to human agents, or a built-in fallback mechanism?

Going Single-Agent vs Multi-Agent: What’s Right for You?

Single-Agent: Single-agent systems work well for smaller, more focused applications such as handling a singular customer service channel or performing a single administrative task. They are simpler to deploy and manage but may end up lacking the flexibility to scale across different domains or departments, especially if you’re considering adding functionalities in the future.

Multi-Agent: If your business application is highly nuanced and requires a complex workflow, splitting up the prompts and tools across multiple agents is likely to give you better performance and make scaling easier.

Here’s a clue – agents failing to follow complicated instructions or regularly selecting incorrect tools. This might be the catalyst for you to move towards multi-agent systems in AI – dividing your system between multiple distinct agents.

Integrating Tools and APIs into Agentic Frameworks

Integrating the right agentic framework tools and APIs seamlessly can mean the difference between an intervention magnet and a well-oiled machine. Marketing platforms, CRM systems, real-time stock market indices, live weather maps, even global currency rates and commodity prices – today’s globalised economy means business operations must account for more variables than ever before. Such tools and APIs allow agents to access real-time data as well as perform actions such as updating records, sending emails, performing accurate and rapid data analysis, scheduling appointments or follow-ups, and making decisions based on the latest data.

Common API integrations:

  • Payment Gateways
  • Email Systems
  • Data Analytics Platforms
  • Calendar Softwares
  • Search APIs like Google Search API
  • Specialised Industry-specific Data APIs

Managing State and Memory for Long-Term Agentic Success

While LLMs excel at processing data, retaining info across interactions is not inherent. Here’s where State Management (the ability to ‘remember’ previous interactions, tasks, or decisions) comes in. This is especially critical for complex and ongoing tasks like customer service, where AI agents have to recall past conversations with particular users to provide contextually relevant help.

Memory management is pivotal to long-term success, letting your system store relevant data that agents could access to shape personalised responses or future actions. State and Memory Management in AI Agents elevate system functionality and bring them closer to human levels of learning and evolution.

Some Good Practices:

  • Persistent States: Use cloud storage or databases to save state information as data between interactions so as to persist the state.
  • Data Privacy: Make sure sensitive data is securely stored and that agentic operations comply with data privacy regulations like GDPR and HIPAA.
  • Efficiency: Utilise memory techniques that allow AI to discard irrelevant information to prevent bloated databases and slow performance.

A Look at Popular Agent-Based Frameworks on the Market

Several open-source offerings have emerged to help build reliable AI agents that can effectively handle critical applications in diverse fields for organisations. Here’s a look at some popular Agentic Frameworks available:

1) LangChain

A versatile open-source library for building agentic systems, offering modular components like models, prompts, memory, indexes, and chains to streamline complex application development. It supports multiple LLMs, enables integration with various tools, and includes LangSmith for debugging and monitoring. Ideal for prototyping general-purpose LLM applications with diverse data sources and tool connections.

2) CrewAI

CrewAI focuses on orchestrating collaborative, role-based autonomous agents, allowing each to have distinct roles, histories, and tools. It manages task delegation and supports complex collaboration patterns, including sequential and hierarchical workflows, with extensibility for multiple LLMs and toolsets. Best suited for multi-agent systems tackling decomposed or simulated specialized tasks.

3) AutoGen

Developed by Microsoft, AutoGen streamlines multi-agent conversation orchestration through agents that communicate via messages. It supports complex interaction patterns, integrates human input, and emphasizes optimization and benchmarking through tools like AgentBench. Designed for developing advanced multi-agent workflows and research in collaborative AI conversations.

4) LangGraph

An extension of LangChain, LangGraph models agentic workflows as stateful graphs, supporting advanced control flows like loops and branching. It manages state transitions between nodes, allows persistence and streaming, and is well-suited for building complex, reliable multi-step or human-in-the-loop processes beyond linear chains.

5) Semantic Kernel

Microsoft’s Semantic Kernel bridges AI models with traditional programming using modular ‘Skills’ composed of prompts or native code. It features robust memory, connectors to tools and models, and planners that sequence skills toward user goals, all managed by a central orchestrator. Best for developers in the Microsoft ecosystem aiming to embed LLMs into existing applications.

Build a Custom Agent vs Buy a Pre-Built Solution: What should you do?

The decision between building a custom agentic AI solution and purchasing a pre-built framework depends largely on your organization’s needs and resources.

Build Your Own:

  • When You Need Specific Customization: If your business operations require specialized workflows or integrations not offered by out-of-the-box solutions, building a custom agent may be the best choice.
  • When You Have Unique Data: If you have proprietary data that’s crucial for the agent’s functionality, a custom solution can be built to handle this data securely.

Buy a Pre-Built Solution:

  • When Time-to-Market is Critical: Pre-built solutions offer faster deployment, often requiring minimal customization.
  • When Budget is a Concern: Building a custom solution can be expensive and time-consuming, whereas pre-built frameworks offer a cost-effective option for smaller or less complex use cases.

Why are Agentic Solutions so hard to get right?

Building an agent prototype is easy! Indeed, ask most agent builders and that’s the answer you’ll get. So why do businesses still hesitate to cross the agentic Rubicon?

The answer is consistency, or rather the lack of it. Making a functioning demo where you’ve vetted all that goes in is relatively straightforward. But what happens when you throw it into the deep end against vague user inputs, incomplete system messages, wrong tools, bad tool descriptions, incorrectly formatted tool responses, and most importantly, the LLM not receiving the right context?

“Everyone has a plan till they get punched in the mouth.” – Mike Tyson

And just like facing prime Tyson, agents driving business-critical applications can’t afford to get unlucky, not even once.

Concluding thoughts

As LLMs continue to evolve and improve, their role in business operations will only deepen. Expect more sophisticated decision-making abilities, better integration with enterprise tools, and even more autonomous workflows. Shortly, agentic AI might become a standard part of every organization’s technology stack, helping businesses not just respond to queries but actively drive and execute key business processes.

Agentic Frameworks are a game-changer for businesses aiming to automate and enhance operations. Ask yourself – has your org truly cracked the code to autonomous decision-making? Are you getting the most out of agentic AI?

Roxiler Systems specializes in bridging the gulf between great concepts and cold reality, bringing dream solutions into fruition. AI native solutions like agentic automation demand deep understanding and years of real-world experience. We solve these technology bottlenecks so you can focus on what matters – growth.

Get in touch with us to explore bespoke AI-powered solutions!

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