The beginner’s guide to agentic AI frameworks
AI agents are intelligent systems that autonomously perform tasks on behalf of a user. They can reason, connect to tools and data sources, and even collaborate with other agents to achieve the goals set for them. For enterprises, this shift from reactive to proactive AI heralds a new era in customer experience and operational efficiency.
For all their autonomy, however, AI agents also represent a new level of IT complexity. For teams lacking in deep AI expertise, agentic AI development can be downright daunting. In fact, over 80% of AI projects fail to reach production, often due to insufficient tooling, limited in-house skills, or complex integrations.
This is where agentic AI frameworks come in.
Rather than building AI agents from scratch, developers can use agentic frameworks for a faster, more scalable approach. Frameworks provide the pre-built structures and tools to create complex AI systems in which multiple agents can interact, communicate, and collaborate toward a common goal.
A growing set of agentic AI frameworks are available to enterprises looking to rapidly design, test, and deploy AI agents. This guide explains what these frameworks are, how they work, the advantages they bring, and the challenges to watch for when adopting them. You’ll learn how to choose the right framework for your artificial intelligence (AI) use cases, helping to streamline and safeguard the process of AI agent development, governance, and accountability.
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What are agentic AI frameworks?
An agentic AI framework is a software toolkit that simplifies the design, deployment, and management of AI agents. Rather than starting from scratch, developers get an infrastructure layer and pre-built components that help AI agents perform goal-directed tasks, connect to external systems, and scale efficiently.
Basically, agentic frameworks define the rules and protocols for how AI agents think, act, and interact with systems, users, and other agents in their environment. Part building blocks for agentic systems, part blueprint for guiding agentic workflows—these frameworks provide the structures and mechanisms AI agents need to:
Reason about their environment (understand and interpret context)
Coordinate their actions (plan their actions, work together on tasks)
Decide and act (choose the next best step toward a goal)
Communicate with configured systems, users, and other AI agents
In short, agentic AI frameworks provide the foundational structure for developing autonomous AI systems. They equip developers with the tools to build custom AI agents that operate effectively, while also helping to streamline development and accelerate into production.
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Core features of agentic AI frameworks
Agentic AI frameworks provide the building blocks for developing AI agents that can act on their own across complex digital ecosystems. Their core features include:
Pre-built modular components: Ready-made components and design patterns accelerate customization, upgrades, and scaling AI agents, without rebuilding from scratch.
Prompt management & structuring: Frameworks standardize how AI prompts are written to ensure consistent output formats, and parse responses to route them to the right tools or APIs.
Planning & orchestration: Enables agents to break down goals, strategize steps, and coordinate actions, whether individually or as part of a multi-agent workflow.
Communication protocols: Define how agents interact with humans and software systems, supporting AI agent collaboration, agentic workflow coordination, and task prioritization.
Tool & API integration: Provides plug-and-play connectivity with external systems (such as databases, APIs, and AI agent integrations), so agents can act seamlessly and personalize experiences using real-time information.
Memory & context management (Knowledge): Supports both short-term task memory and long-term knowledge retention, often with retrieval-augmented generation (RAG) for accurate context use.
Monitoring & debugging: Includes AI observability tools for tracking AI agent performance, diagnosing errors, and improving reliability.
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Benefits of using agentic AI frameworks
Agentic AI frameworks can help organizations to more quickly build scalable, efficient agentic systems. This leads to many advantages:
Reduced development time and cost
Agentic AI frameworks come with pre-built components, libraries, templates, and tools so teams don’t have to reinvent every agent from scratch. Rather than hand-coding system prompts and integrations, developers can focus on spinning up new agents, iterating, and innovating.
Scalability by design
Frameworks are built to handle large datasets, complex workflows, and multiple agents running in parallel. They make it easy to scale from a single agent to a fleet of specialized AI agents that each handle a unique business task.
Improved efficiency
By accelerating development and automating repetitive tasks, agentic frameworks free human teams to focus on higher-impact work, leading to increased human productivity and reliable agent operations.
Consistency and accuracy
Standardized structures, reasoning engines, and memory systems help agents produce uniform results across tasks—crucial to a quality AI customer experience. Continuous learning and error-handling reduce the variability of agent outputs over time, minimizing common mistakes of some manual agentic AI implementations.
Seamless integration
Frameworks are designed to connect with existing business systems—APIs, databases, applications—without requiring re-engineering. This makes it much easier and more cost-effective to embed AI agents into current enterprise workflows and processes.
Observability and trust
Choose frameworks with built-in monitoring, logging, and AI agent evaluation to ensure agents operate safely and responsibly. AI agent observability tools should make it clear what an agent did, how it did it, and why—providing the transparency needed to ensure AI trust and safety.
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How to evaluate an agentic AI framework
Agentic AI frameworks aren’t one-size-fits-all. Each comes with unique strengths and trade-offs, so the key is balancing your business goals, technical needs, and long-term scalability. A good evaluation process looks at both what to look for in a framework and how to make the right choice for your enterprise.
Key factors to consider
Scalability: Choose a framework that can handle both more agents and more complex tasks as your needs evolve. This ensures you won’t outgrow your architecture as workloads and applications scale.
Flexibility: Look for support across different AI models, algorithms, and techniques. Flexibility makes it easier to adapt to new challenges and adopt emerging technologies without lock-in.
Interoperability: Strong frameworks enable seamless communication between agents, enterprise systems, and third-party services—key for building multi-agent environments and integrating with existing tools.
Security, privacy & compliance: Look for safeguards like encryption, two-factor authentication, and role-based access controls, plus compliance with key regulatory standards to ensure trustworthy, responsible operations.
Ease of use: Clear APIs, robust documentation, and prebuilt templates help reduce developer effort. Monitoring dashboards, debugging tools, and observability hooks make it easier to deploy, manage, and optimize agents quickly and reliably.
Steps to making the right choice
Define your needs – Identify your core use cases and align them with the strengths of different frameworks (e.g., conversational AI voice agents vs. back-office orchestration).
Assess compatibility – Ensure the framework integrates smoothly with your existing APIs, data infrastructure, and systems.
Evaluate tools and support – Consider the strength of the developer community, enterprise SLAs, vendor backing, and roadmap.
Test with a pilot project – Run a small-scale proof of concept to validate performance, reliability, and developer experience before committing.
Pro tip: If you’re trying to determine which AI model is best suited as the base LLM for your AI agent, you can refer to Hugging Face’s agent leaderboard.
Examples of agentic AI frameworks
Agentic AI frameworks are evolving rapidly, along with the autonomous agents they enable. Many popular frameworks exist, each offering its own unique methodologies and features.
What agentic AI framework should you use? Here are some of the most widely used:
AutoGen (Microsoft)
Flexible, open-source programming framework for scalable, event-driven multi-agent orchestration. Supports asynchronous messaging, observability tools, and includes developer-focused utilities for benchmarking and no-code prototyping. You can access on GitHub.
CrewAI
Lightweight orchestration framework for building crews of specialized agents. Define roles, goals, and interaction flows using natural language. Enables cross-agent collaboration with event-driven orchestration. You can access on GitHub.
LangChain
Modular open-source framework with extensive integrations—LLMs, embeddings, vector stores, tools—for seamless prompt chaining, memory, and model integration. Enhanced by LangSmith for debugging, testing, and performance monitoring. You can access on GitHub.
LangGraph
Agent orchestration layer within the LangChain ecosystem. Uses graph-based structure to manage complex, non-linear workflows. Offers persistent state, human-in-the-loop capabilities, observability, retries, and fault tolerance. You can access on GitHub.
LlamaIndex Workflows
Event-driven, asynchronous-first system for dynamic AI agentic workflows. Its model—built on steps, events, and shared context—supports flexible branching, loops, and robust instrumentation for RAG pipelines and multi-step coordination. You can access on GitHub.
Semantic Kernel (Microsoft)
Enterprise-focused SDK available for C#, Python, and Java. Supports function calling, emplated prompts, and multi-agent orchestration via process frameworks. Integrates with RAG for dynamic tool use. You can access on GitHub.
Vellum Workflows SDK
Open-source framework for defining agentic systems as graphs using declarative, type-safe syntax. It offers bi-directional sync between code and a visual editor, advanced control flow (loops, conditionals, parallelism), HITL support, and tools for AI control and AI responsibility. You can access on Github.
What are the best agentic AI frameworks for customer support and CX?
LangChain – For building knowledge-driven agents with strong retrieval and context handling capabilities, ensuring accurate and consistent answers.
AutoGen – For orchestrating multi-agent workflows, like ticket triage, escalation, and follow-up across specialized support roles.
CrewAI – For quickly standing up role-based AI support agents using natural language to define each agent’s purpose and output.
👉 Pair any of these with Sendbird Trust OS governance framework to add enterprise-grade observability, guardrails, and reliability to agents—delivering a seamless customer experience and a scalable foundation for AI trust and safety.
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Agentic AI frameworks vs AI agent builders: What’s the difference?
Although they’re closely related, agentic AI frameworks and AI agent builders serve different purposes in the AI stack.
Agentic AI frameworks: The engine
Frameworks provide the infrastructure for building agentic systems, and this foundation handles the heavy lifting for developers. However, frameworks are powerful and require a measure of technical expertise and familiarity with distributed systems.
AI agent builders: The dashboard layer
AI agent builders harness the power of frameworks for non-technical teams in the real world. They offer intuitive interfaces and prebuilt workflows for practical use cases, enabling organizations to quickly build, launch, and scale AI agents—without needing to master the underlying framework.
The right AI agent builder does more than simplify the creation of AI agents. They bring enterprise-readiness to agentic systems, embedding security, compliance, monitoring, and integrations with existing systems—helping to quickly and safely turn AI pilots into successful, scalable AI implementations.
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The key role of agentic AI governance frameworks
Frameworks make it easier to build effective agentic AI systems, but most aren’t designed with enterprise-level trust and safety requirements in mind. This is where agentic AI governance frameworks are helpful.
Basically, agentic AI governance frameworks are the trust and safety layer that makes AI agents enterprise-ready. For example, in AI support use cases, governance frameworks help ensure that agents deliver accurate, safe, and consistent responses to customers at all touchpoints.
With Sendbird’s Trust OS framework, for example, enterprises gain:
AI transparency: Real-time observability capabilities reveal the logic, data, and context behind an agent’s output, and corresponding user outcomes, to enable visibility and performance optimization.
AI governance: To help teams move fast without incurring risk, these frameworks provide tools to manage agent changes with precision across teams before and after deployment, including versioning and access controls.
AI oversight: Teams must monitor and resolve agent issues in real time to ensure trust and safety. Frameworks offer dashboards with real-time KPIs, AI agent testing, fallback strategies, and guardrails to ensure consistent performance at scale.
AI scalability: A good governance framework has a trusted omnichannel infrastructure that’s both agent-ready and enterprise-ready. It helps teams manage and scale omnipresent AI agents across different languages, markets, and contexts, without excessive maintenance or CX-eroding latency.
Agentic governance frameworks like Sendbird’s Trust OS pick up where agentic frameworks often leave off—providing teams with tools to ensure AI agents operate safely, responsibly, and reliably in high-stakes enterprise settings. They help you safely develop and scale AI agents, without slowing down or incurring unnecessary risk.
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