The rise of AI agents will reshape how intelligent systems are being built. According to a recent PagerDuty survey , over half of companies are already deploying “agentic AI” solutions, and by 2027, as many as 86% expect to be operational with AI agents. Organizations see a high upside: 62% of leaders expect triple-digit ROI from agentic AI, often by automating 26–50% of workloads . But building effective agents requires more than just powerful models—it demands smart orchestration of memory, tools, prompts, and actions to make AI truly useful.
Two frameworks have emerged as leading contenders in this space: LangChain and AutoGen. While both are open-source and designed to streamline large language model (LLM) applications, they take fundamentally different approaches to solving the same problem—how to make AI agents smarter, faster, and more autonomous.
So, which one should you choose?
Choosing the right agent framework could save weeks of development and boost model efficiency. In this blog, we break down the core differences between AutoGen and LangChain to help you make the right call.
What Is LangChain? Founded by Harrison Chase in 2022 as an open-source framework. Originally developed to address the complexity of building applications with Large Language Models (LLMs). It emerged from the need to standardize and simplify LLM application development . Moreover, quickly gained traction in the AI development community due to its modular approach
Core Purpose
Provides modular orchestration of LLMs with integrated tools, memory, and processing chains Enables developers to build complex AI applications without starting from scratch Abstracts away the complexity of connecting different AI components and external services Facilitates seamless integration between language models and real-world data sources
Key Components 1. Agents
Autonomous entities that can reason about problems and take actions Make decisions about which tools to use based on user input and context Can chain multiple actions together to accomplish complex tasks 2. Chains
Pre-built sequences of operations that process inputs through multiple steps Provide reusable patterns for common LLM application architectures 3. Prompt Templates
Standardized formats for structuring inputs to language models Allow dynamic insertion of variables and context into prompts Ensure consistent and optimized communication with LLMs 4. Tools
External integrations including search engines, calculators, databases, and APIs Enable LLMs to access real-time information and perform actions beyond text generation Extensible ecosystem allowing custom tool development Popularity and Ecosystem Widely adopted across industries for rapid AI application development, LangChain also offers strong enterprise integrations with major cloud providers and AI platforms. In addition, it benefits from a thriving open-source community with thousands of contributors. Furthermore, it features extensive documentation and community-driven tutorials and examples, making it accessible for developers at all levels.
Typical Use Cases 1. Conversational Agents
Building sophisticated chatbots with memory and context awareness Creating customer service agents that can access company databases 2. Retrieval-Augmented Generation (RAG)
Combining LLMs with proprietary knowledge bases for accurate, up-to-date responses Building document query systems that can search and synthesize information Creating AI systems that can reference specific company or domain knowledge 3. Data Enrichment and Summarization
Building systems that can process, categorize, and summarize business data
What Is AutoGen? AutoGen is a multi-agent conversation framework developed by Microsoft Research that enables sophisticated collaboration between large language models (LLMs) through structured messaging and workflow automation .
Core Architecture Multi-agent conversation system : Multiple AI agents communicate and collaborate to solve complex problems Conversable agents : Each agent can send and receive messages, maintaining context across interactions Goal-oriented workflows : Agents work together toward specific objectives through coordinated task execution Open-source framework : Freely available for developers and researchers to implement and customize
Key Features 1. Agent Collaboration
Agents can assume different roles (critic, executor, reviewer) within the same workflow Dynamic conversation flows that adapt based on task requirements and intermediate results Built-in conflict resolution and consensus-building mechanisms 2. Human Integration
Human-in-the-loop capabilities : Seamless integration of human oversight and intervention Manual approval gates for critical decisions or quality control checkpoints 3. Task Orchestration
Complex multi-step task breakdown and delegation across specialized agents Automated workflow management with error handling and retry mechanisms
Common Applications
1. Development and Analysis
Document analysis : Collaborative review, summarization, and fact-checking workflows Research synthesis : Agents gather, analyze, and synthesize information from multiple sources 2. Business Workflows
Autonomous decision-making : Multi-perspective analysis for complex business scenarios Content creation : Coordinated writing, editing, and refinement workflows AutoGen represents a significant advancement in AI orchestration, moving beyond single-agent interactions toward collaborative intelligence systems that can tackle sophisticated, multi-faceted challenges.
AutoGen vs LangChain: Core Differences Category LangChain AutoGen Developer/Origin Developed by LangChain Inc., open-source Developed by Microsoft Research, open-source Architecture Style Modular chains and agents Multi-agent conversation graph Primary Paradigm Sequential tool and prompt orchestration Conversational, goal-oriented agent collaboration Use Case Focus RAG pipelines, chatbots, tool integration Autonomous coding, document processing , multi-agent systems Human-in-the-Loop Requires manual implementation Built-in support for human-AI collaboration Ease of Use Moderate; rich documentation and templates Steeper learning curve; requires configuration of agent roles Tool Integration Strong ecosystem; supports many tools (e.g., Pinecone, Chroma, OpenAI) Agents can call tools/functions based on defined roles Memory & State Handling Uses external memory modules (e.g., buffer, vector store) Internal memory management between agents Customization Level High (chain-level and agent-level logic customization) High (agent role and behavior customization) Community Ecosystem Large, active community; integrations with LangSmith, LangServe Growing adoption; strong GitHub presence; Microsoft-backed Deployment Tools LangServe for API deployment; LangSmith for debugging Manual deployment or custom orchestration required Debugging Support LangSmith for tracing and prompt management Logging through conversation tracking Best Fit For Developers creating structured, prompt-driven workflows Teams building autonomous, multi-step workflows Learning Curve Beginner to Intermediate Intermediate to Advanced Enterprise Maturity Widely used in production pipelines Suitable for advanced, research-oriented or enterprise R&D scenarios
When to Choose LangChain?
1. Developer-Friendly Entry Point Perfect for LLM newcomers : Provides intuitive abstractions that simplify complex AI integration workflows Structured task management : Excels at applications requiring sequential prompt chains and logical workflow progression Rapid learning curve : Comprehensive documentation and community resources accelerate development timelines
2. Core Application Strengths RAG (Retrieval-Augmented Generation) pipelines : Built-in components for document indexing, retrieval, and context injection Chatbot development : Pre-built templates and conversation management tools for interactive applications Sequential processing : Ideal for multi-step tasks requiring coordinated prompt execution and response handling
3. Extensive Third-Party Compatibility Vector databases : Seamless integration with Pinecone, ChromaDB, Weaviate, and other embedding storage solutions Model providers : Native support for Hugging Face, OpenAI, Anthropic, and numerous LLM providers Data connectors : Built-in loaders for PDFs, web scraping, databases, and various document formats
4. Professional Development Tools LangSmith : Comprehensive debugging and monitoring platform for tracking agent performance and conversation flows LangServe : Production deployment framework that simplifies API creation and scaling Observability features : Built-in logging, tracing, and performance analytics for production environments
5. Prototyping Excellence Rapid experimentation : Modular components enable quick testing of different approaches and configurations Template library : Pre-built patterns for common use cases reduce development time significantly Community ecosystem : Active developer community provides solutions, examples, and troubleshooting support
6. Production Readiness Scalable architecture : Designed to handle enterprise-level workloads with proper resource management API-first design : Easy transition from prototype to production-ready services Monitoring integration : Built-in tools for performance tracking and system health monitoring LangChain represents the optimal choice for teams seeking a balance between ease of use, comprehensive features, and production scalability in LLM application development.
When to Choose AutoGen?
1. Multi-Agent Collaboration Excellence Autonomous agent networks : Perfect for scenarios requiring multiple AI agents to work independently while maintaining coordination Complex task decomposition : Excels at breaking down sophisticated challenges into manageable subtasks distributed across specialized agents Goal-driven workflows : Ideal for organizations implementing outcome-focused automation where agents collaborate toward specific objectives
2. Advanced Team Dynamics Distributed problem-solving : Enables different agents to assume complementary roles (analyst, critic, executor) within unified workflows Collaborative intelligence : Facilitates emergent solutions through agent interaction and collective decision-making processes Dynamic role allocation : Agents can adapt their responsibilities based on task requirements and real-time workflow needs
3. Built-in Conversation Management Persistent memory systems : Agents maintain context and learning across extended interactions and project lifecycles Dialogue tracking : Comprehensive conversation history enables agents to reference previous decisions and maintain workflow continuity Inter-agent communication : Sophisticated messaging protocols ensure clear information exchange and coordination
4. Enterprise-Ready Features Human oversight integration : Seamless handoffs between automated processes and human supervision for critical decision points Scalable architecture : Designed to handle complex organizational workflows with multiple concurrent agent conversations Audit trails : Complete tracking of agent interactions and decision-making processes for compliance and optimization
5. Development Scenarios Code review partnerships : Specialized agents handle coding, testing, and review processes collaboratively Quality assurance teams : Multiple agents perform different validation steps while sharing findings and recommendations Research synthesis : Agents gather, analyze, and synthesize information from diverse sources through coordinated investigation
6. Enterprise Automation Multi-departmental workflows : Agents represent different business functions while maintaining organizational alignment Decision support systems : Collaborative analysis providing multiple perspectives on complex business challenges Process optimization : Continuous workflow improvement through agent learning and adaptation AutoGen represents the premier choice for organizations seeking sophisticated multi-agent AI systems that can handle complex, goal-oriented tasks requiring genuine collaboration and human integration.
Real-World Use Cases: AutoGen vs LangChain
LangChain Applications: 1. Financial Data Intelligence Automated financial summarizer : Processes quarterly reports, earnings calls, and market data to generate executive-level summaries with key metrics and trends Investment research assistant : Combines company filings, news sentiment, and market indicators to provide comprehensive investment analysis Risk assessment pipeline : Sequential processing of multiple data sources to evaluate portfolio risk and regulatory compliance
2. Healthcare and Medical Support Patient triage assistant : Analyzes symptoms and medical history to prioritize cases and suggest appropriate care pathways Drug interaction checker : Cross-references medication databases to identify potential conflicts and contraindications
3. Legal Document Processing Contract analysis with RAG : Extracts key terms, identifies potential risks, and compares clauses against legal precedents stored in vector databases Legal research automation
AutoGen Applications: 1. Software Development Collaboration Code review orchestration : Multiple specialized agents handle syntax checking, security analysis, performance optimization, and documentation review Testing automation teams : Coordinated agents create test cases, execute validation, and generate comprehensive quality reports
2. Document Management Systems Multi-stage validation pipeline : Reader agents extract and structure content while Reviewer agents verify accuracy, completeness, and compliance standards Content quality assurance : Collaborative editing where different agents focus on grammar, fact-checking, formatting, and style consistency Translation and localization : Specialized agents handle translation, cultural adaptation, and quality review for multilingual content
3. Customer Service Automation Intelligent query triaging : Multiple agents analyze customer requests, determine priority levels, route to appropriate departments, and escalate when necessary Multi-channel support coordination : Agents manage simultaneous conversations across email, chat, and phone while maintaining context and consistency Resolution tracking : Collaborative problem-solving where agents document issues, research solutions, and coordinate follow-up activities These implementations demonstrate how LangChain excels in structured, single-workflow applications while AutoGen shines in complex, collaborative scenarios requiring genuine multi-agent coordination and sophisticated task delegation.
Integration & Extensibility: AutoGen vs LangChain LangChain Integration Ecosystem:
1. Database and Storage Solutions Vector database compatibility : Seamless integration with Pinecone, ChromaDB, Weaviate, Qdrant, and FAISS for embedding storage and retrieval Traditional databases : Native connectors for PostgreSQL, MongoDB, Redis, and other structured data sources Cloud storage integration : Direct access to AWS S3, Google Cloud Storage , and Azure Blob Storage for document processing
2. Model Provider Flexibility LLM provider support : Built-in adapters for OpenAI, Anthropic, Cohere, Hugging Face, and local model deployments Embedding services : Integration with OpenAI embeddings, Cohere embeddings, and sentence transformers Multi-modal capabilities : Support for image, audio, and text processing through various provider APIs
3. Deployment and Scaling LangServe deployment : Production-ready API serving with automatic scaling and load balancing Monitoring integration : Built-in observability through LangSmith for performance tracking and debugging Container orchestration : Docker and Kubernetes compatibility for enterprise deployments
AutoGen Integration Capabilities:
1. Model Flexibility and Deployment Multi-provider support : Compatible with OpenAI, Azure OpenAI, Google Vertex AI, and local LLM deployments Cost optimization
2. Tool and Function Integration Python function calling : Agents can execute custom Python functions and interact with existing codebases External API integration : Built-in capabilities for REST API calls, database queries, and third-party service interactions Custom tool development : Framework for creating specialized agent tools and extending functionality
3. Enterprise System Connectivity Authentication systems : Support for enterprise SSO, OAuth, and custom authentication mechanisms Audit and compliance : Built-in logging and tracking for regulatory requirements and security monitoring Both frameworks prioritize extensibility, with LangChain excelling in data source integration and deployment tooling, while AutoGen focuses on flexible agent architectures and enterprise system connectivity.
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Challenges and Limitations: AutoGen vs LangChain
LangChain Limitations: Chain complexity escalation : Nested chains and complex workflows can become difficult to maintain and understand as projects scale Abstraction overhead : Multiple layers of abstraction can obscure underlying processes, making troubleshooting challenging Performance bottlenecks : Sequential chain execution can create latency issues in complex multi-step workflows Agent behavior opacity : Debugging why specific decisions were made or chains failed can be extremely difficult Error propagation : Failures in one chain component can cascade unpredictably through the entire workflow Version compatibility : Rapid framework evolution can break existing implementations and require frequent updates Resource consumption : Memory usage can grow significantly with complex chain structures and large context windows Monitoring complexity : Tracking performance across multiple chains and components requires sophisticated observability tools
AutoGen Limitations: Higher technical barrier : Requires deeper understanding of multi-agent systems and conversation design patterns Complex setup requirements : Initial configuration and architecture decisions demand more planning and expertise Limited beginner resources : Fewer tutorials and guided examples compared to more established frameworks Documentation limitations : Less comprehensive documentation and fewer community-contributed resources than LangChain Component availability : Fewer pre-built, plug-and-play components requiring more custom development work Community support : Smaller developer community means fewer stackoverflow answers and third-party extensions Agent coordination complexity : Managing multiple agents and their interactions can introduce unpredictable behaviors Debugging multi-agent systems : Tracing issues across multiple conversing agents is inherently more complex than single-agent debugging Production deployment : Less mature deployment tooling and monitoring solutions for multi-agent architectures Both frameworks face the fundamental challenge of balancing powerful capabilities with developer experience, though they encounter different trade-offs in their respective approaches to LLM application development.
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FAQs Which is better, LangChain or AutoGen? Neither framework is universally better; the right choice depends on your use case. LangChain excels at building LLM-powered pipelines with extensive integrations and retrieval-augmented generation workflows. AutoGen is purpose-built for multi-agent collaboration where autonomous agents communicate and execute tasks together. For single-agent applications with complex tool chaining, LangChain typically fits better. For orchestrating multiple AI agents tackling complex problems, AutoGen delivers stronger results. Kanerika’s agentic AI specialists assess your requirements and recommend the optimal framework architecture for your enterprise needs.
What is the main difference between AutoGen and LangChain? The main difference lies in architectural philosophy. LangChain focuses on chaining LLM calls with tools, memory, and retrievers in sequential or branching workflows. AutoGen centers on conversational multi-agent systems where multiple AI agents interact autonomously to solve problems. LangChain provides granular control over each pipeline step, while AutoGen abstracts agent communication patterns for collaborative task completion. LangChain suits RAG applications and tool-augmented assistants; AutoGen fits scenarios requiring agent-to-agent dialogue. Kanerika helps enterprises select and implement the right AI agent framework for their specific automation goals.
When to use AutoGen vs LangGraph? Use AutoGen when building conversational multi-agent systems where agents autonomously collaborate through natural dialogue. Choose LangGraph when you need fine-grained control over stateful, cyclic workflows within the LangChain ecosystem. LangGraph offers explicit graph-based orchestration with customizable nodes and edges, making it ideal for complex conditional logic. AutoGen simplifies agent coordination but provides less workflow customization. For production applications requiring deterministic state management, LangGraph often proves more controllable. Kanerika’s AI engineering team evaluates your workflow complexity to recommend whether AutoGen or LangGraph best serves your use case.
What is the difference between AutoGen and CrewAI vs LangChain? AutoGen, CrewAI, and LangChain serve different multi-agent and LLM orchestration needs. LangChain provides modular building blocks for LLM applications including chains, agents, and retrievers. AutoGen enables conversational multi-agent collaboration through autonomous agent interactions developed by Microsoft Research. CrewAI focuses on role-based agent teams with defined responsibilities and sequential or hierarchical task delegation. LangChain offers the broadest integration ecosystem, while AutoGen and CrewAI specialize in agent collaboration patterns. Kanerika architects custom agentic AI solutions leveraging the optimal framework combination for your enterprise workflows.
Is AutoGen production ready? AutoGen has matured significantly and is used in production environments, though careful evaluation remains essential. Microsoft continues active development with regular releases improving stability and features. Production readiness depends on your specific requirements around error handling, observability, and scale. Organizations deploy AutoGen successfully for internal automation, research workflows, and customer-facing applications with appropriate guardrails. Key considerations include implementing robust logging, human oversight mechanisms, and fallback strategies. Kanerika helps enterprises assess AutoGen production readiness and build the monitoring infrastructure needed for reliable multi-agent deployments.
Which framework is better for autonomous task execution? AutoGen typically excels at autonomous task execution involving multiple collaborating agents that communicate and iterate without constant human intervention. Its conversational agent architecture enables agents to delegate subtasks, verify outputs, and self-correct through dialogue. LangChain provides strong autonomous capabilities through its agent executor and tool-use patterns but requires more explicit orchestration logic. For complex tasks needing multiple specialized agents working together autonomously, AutoGen’s native multi-agent design offers advantages. Kanerika deploys autonomous AI agents using the framework best suited to your task complexity and control requirements.
Is AutoGen free to use? AutoGen is free and open-source under the MIT license, allowing commercial and non-commercial use without licensing fees. Microsoft Research developed and maintains the framework publicly on GitHub. While the framework itself costs nothing, you pay for underlying LLM API calls to providers like OpenAI or Azure OpenAI. Infrastructure costs for hosting agents and any compute resources for code execution add to total cost of ownership. The permissive license enables enterprise adoption without vendor lock-in concerns. Kanerika helps organizations optimize AutoGen implementations to minimize API costs while maximizing multi-agent performance.
What are alternatives to AutoGen? Leading AutoGen alternatives include LangChain and LangGraph for LLM orchestration, CrewAI for role-based agent teams, and Microsoft Semantic Kernel for enterprise AI integration. LlamaIndex specializes in retrieval-augmented generation workflows. Haystack offers modular NLP pipelines with agent capabilities. OpenAI’s Assistants API provides managed agent functionality without self-hosting. For simpler use cases, direct LLM API integration may suffice. Each alternative trades off flexibility, ease of use, and multi-agent sophistication differently. Kanerika evaluates your requirements across these multi-agent frameworks to recommend and implement the optimal solution for your enterprise.
Does AutoGen support human-in-the-loop workflows? AutoGen fully supports human-in-the-loop workflows through its UserProxyAgent class, which enables human intervention at defined points during agent conversations. You can configure agents to request human approval before executing code, making critical decisions, or proceeding past checkpoints. This design allows seamless transitions between autonomous execution and human oversight based on task sensitivity. Human feedback integrates directly into the multi-agent conversation flow, enabling corrections and guidance without breaking workflow continuity. Kanerika implements human-in-the-loop AutoGen architectures that balance automation efficiency with appropriate human governance for enterprise compliance.
Is AutoGen a framework or platform? AutoGen is an open-source framework, not a hosted platform or managed service. It provides Python libraries and abstractions for building multi-agent conversational AI systems that you deploy on your own infrastructure. Unlike SaaS platforms, AutoGen requires you to handle hosting, scaling, and operations. This framework approach offers maximum flexibility and control over agent behavior, data privacy, and LLM provider selection. You integrate AutoGen into your existing Python applications and infrastructure stack. Kanerika helps enterprises architect and deploy AutoGen-based solutions on cloud infrastructure optimized for your security and performance requirements.
Can I use both LangChain and AutoGen together? Yes, LangChain and AutoGen can be combined effectively in hybrid architectures. Common patterns include using LangChain tools and retrievers within AutoGen agents, leveraging LangChain’s extensive integration ecosystem while benefiting from AutoGen’s multi-agent orchestration. You might build RAG pipelines with LangChain and expose them as capabilities to AutoGen agents. Some teams use LangChain for single-agent workflows and AutoGen when tasks require agent collaboration. Integration requires thoughtful architecture to avoid complexity without clear benefits. Kanerika designs hybrid LangChain-AutoGen implementations that leverage each framework’s strengths for your specific use cases.
Is LangChain easier to learn than AutoGen? LangChain generally presents a steeper initial learning curve due to its extensive abstraction layers, numerous components, and rapidly evolving API. AutoGen’s conversational agent model can feel more intuitive for developers familiar with chat-based interactions. However, LangChain’s comprehensive documentation, larger community, and abundant tutorials offset its complexity. AutoGen’s multi-agent concepts require understanding agent communication patterns that may be unfamiliar. Both frameworks reward investment with powerful capabilities. Your background in LLM development influences which feels more natural. Kanerika accelerates your team’s proficiency through hands-on training and implementation guidance for either framework.
Which has better tool integration and plugin support? LangChain offers significantly broader tool integration and plugin support through its mature ecosystem of connectors, document loaders, vector stores, and third-party integrations. The LangChain community has built hundreds of integrations covering databases, APIs, cloud services, and productivity tools. AutoGen provides solid tool integration capabilities but with a smaller pre-built ecosystem, requiring more custom development for specialized connections. LangChain’s tool abstraction layer makes adding new integrations straightforward. For projects requiring extensive external system connectivity, LangChain typically accelerates development. Kanerika builds custom tool integrations for both frameworks tailored to your enterprise systems landscape.
Why do we use AutoGen? Teams use AutoGen to build sophisticated multi-agent AI systems where autonomous agents collaborate through conversation to solve complex problems. AutoGen simplifies creating agent teams that can plan, execute code, debate solutions, and iteratively refine outputs without extensive orchestration code. Its conversational paradigm maps naturally to workflows requiring multiple specialized perspectives. AutoGen excels at research automation, code generation with self-verification, complex analysis tasks, and scenarios where agent collaboration improves outcomes over single-agent approaches. Microsoft’s backing provides confidence in continued development. Kanerika implements AutoGen-powered solutions that transform how enterprises automate knowledge-intensive workflows.
Can AutoGen execute code? AutoGen can execute code through its built-in code execution capabilities, a distinguishing feature enabling agents to write, run, and iterate on code autonomously. The framework supports secure code execution in Docker containers or local environments with configurable restrictions. Agents can generate Python code, execute it, observe outputs, and refine their approach based on results. This capability powers use cases like data analysis, automated testing, and software development assistance. Proper sandboxing and security controls are essential for production deployments. Kanerika configures secure AutoGen code execution environments with appropriate guardrails for enterprise deployment scenarios.
Is AutoGen developed by Microsoft? Yes, AutoGen is developed by Microsoft Research and maintained as an open-source project under the MIT license. The framework emerged from Microsoft’s research into multi-agent conversational AI systems and receives active development from Microsoft engineers and the open-source community. Microsoft’s involvement provides resources for ongoing development, documentation, and enterprise-grade improvements. The framework integrates naturally with Azure OpenAI Service, though it works with any OpenAI-compatible LLM provider. Microsoft’s commitment to AutoGen signals long-term viability for enterprise adoption. Kanerika leverages deep Microsoft partnership expertise to deploy AutoGen solutions integrated with your Azure infrastructure.
Which is more suitable for real-time applications? LangChain generally offers better support for real-time applications through its streaming capabilities, async execution patterns, and lighter-weight single-agent configurations. AutoGen’s multi-agent conversations introduce latency as agents exchange messages and await responses. For user-facing applications requiring sub-second responses, LangChain’s streaming output and optimized execution paths prove advantageous. AutoGen suits scenarios where task completion quality outweighs response speed. Real-time performance depends heavily on LLM provider latency, prompt complexity, and architecture choices in either framework. Kanerika architects low-latency AI solutions selecting and optimizing the appropriate framework for your response time requirements.
Does AutoGen use semantic kernel? AutoGen and Semantic Kernel are separate Microsoft frameworks that can work together but operate independently. AutoGen focuses on multi-agent conversational AI systems, while Semantic Kernel provides orchestration for LLM applications with plugins and planners. You can integrate Semantic Kernel skills as tools within AutoGen agents, combining Semantic Kernel’s function calling with AutoGen’s agent collaboration. Microsoft develops both frameworks with different design philosophies and use cases. Neither requires the other, though hybrid architectures leverage complementary strengths. Kanerika evaluates whether AutoGen, Semantic Kernel, or combined architectures best serve your enterprise AI orchestration needs.
