https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms
Many businesses are intrigued by recent developments in agentic AI and the promise of deploying autonomous digital workers that can reason, plan and execute tasks with limited human oversight.
Outside of the teams found in highly specialized AI vendors, however, few teams possess the capability of building an AI agent ecosystem from scratch, prompting the need for frameworks and platforms for developing agents and operationalizing them as they run. But navigating the landscape of tools in this new marketplace can be tricky. Choosing the best tools requires that businesses align technical requirements with business objectives.
There's an important difference between agentic frameworks and agentic platforms.
Agentic AI frameworks are developer-oriented toolkits that deliver building blocks for creating autonomous AI systems. Frameworks offer flexibility and customization, enabling engineers to construct sophisticated multi-agent systems from the ground up. They typically require significant programming expertise but provide fine control over agent behavior, orchestration patterns and system integrations.
Agentic AI platforms are business-focused and designed for rapid deployment without deep technical expertise. Platforms feature low-code or no-code interfaces and prebuilt integrations, while offering enterprise-grade governance capabilities. They prioritize simplicity, compliance and operational management over technical flexibility.
Many enterprises adopt hybrid approaches, using frameworks for specialized applications and platforms for broader deployment.
Agentic AI frameworks offer the following capabilities that focus on technical depth and customization:
Agentic AI platforms offer the following features that emphasize accessibility and enterprise readiness:
Engineers and software developers should evaluate frameworks based on technical requirements, development complexity and long-term maintainability. There are key evaluation criteria across multiple dimensions that directly affect the successful development and performance of AI agents.
Evaluation begins with assessing capabilities for multi-agent orchestration. Modern applications often require sophisticated coordination between several specialized agents, making frameworks that specifically support multi-agent deployments particularly valuable for complex workflows. Memory management systems are also important because short-term contextual memory and long-term knowledge retention affect the accuracy of an agent's operations.
User-interactive experiences and complex workloads can run on their own for many hours. Frameworks must scale vertically (i.e., more resources per agent) and horizontally (i.e., for more agent instances) while maintaining isolation between these sessions for enterprise security. Error handling, including recovery logic and graceful degradation, is essential for production reliability.
The degree of development complexity varies significantly across frameworks. No-code platforms offer visual workflow builders, and medium-code frameworks create some level of abstraction using the Python language. Meanwhile, advanced frameworks could grant full programmatic control. The learning curve of a framework directly affects time to productivity for development teams, making the quality of documentation and debugging tools key considerations.
The maturity of an ecosystem tends to establish long-term viability. An active base of contributors, commercial backing from companies such as vendors, and sample code for production deployments build confidence in the stability of the framework. Professional services, training programs and active developer communities can accelerate successful agent development and deployment.
The chosen framework should best align with the existing technical infrastructure. API compatibility, authentication mechanisms and connections to cloud platforms will dictate how easily alignment can be achieved. Enterprise system connectivity to CRM, ERP and workflow orchestration tools is particularly important for business-critical applications.
The framework landscape has matured significantly in the last two years, with clear differentiation among established leaders, emerging production-ready options and specialized tools.
Enterprise platforms prioritize accessibility for business users, governance and rapid deployment rather than technical flexibility. Recently, the platform landscape has consolidated around major cloud providers and established enterprise software vendors, including the following.
These platforms distinguish themselves from frameworks due to their visual drag-and-drop builders, extensive connectors, enterprise security out of the box, and, often, multichannel deployment across web, mobile and collaboration platforms.
AI agents can act autonomously and make their own "decisions." Successful implementation requires strategic planning that moves beyond experimentation to programs that align closely with core business priorities.
Businesses should start with low-risk pilots featuring clear ROI potential and a manageable degree of complexity. Focus should be on high-impact use cases that serve as lighthouse projects to encourage companywide adoption.
Cross-functional "squads" have proven very useful, combining business domain experts, AI/MLOps engineers, process designers, IT architects and emerging roles like prompt engineers and agent orchestrators. Programs for skills development should address technical upskilling, especially in agent development, alongside the cultural adaptation needed to build trust in human-AI collaboration.
Some of the more common implementation challenges include the complexity of integrating with legacy systems, agent sprawl leading to ungoverned proliferation, and issues with AI errors and hallucinations. These challenges can be mitigated as teams adopt infrastructure as code for reliable deployment and implement comprehensive monitoring with real-time performance tracking. Emerging best practices include "graduated autonomy" so AI agents learn on the job just like new employees and build trust progressively as teams see their performance improve over time.
The future points toward mainstream multi-agent systems, evolving from reactive assistants to proactive problem-solvers that anticipate needs and take autonomous action.
Ethical considerations demand careful attention to the boundaries of autonomous decision-making, methodologies for explainable AI and mitigation of bias in multi-agent systems. California's proposed CCPA modifications specifically target automated decision-making technology. Increasing regulatory scrutiny requires businesses to implement governance procedures with clear accountability chains, human oversight mechanisms and compliance by design.
The transformation to agentic AI demands a reinvention of processes involving human and agentic coworkers. Success depends on top-down leadership, an adequate allocation of strategic resources and commitment to continuous learning. Businesses that move thoughtfully from pilots to production, while maintaining strong ethical foundations and governance policies, will be well positioned to capture the value that agentic AI promises.
Donald Farmer is a data strategist with 30-plus years of experience, including roles as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.
18 Sep 2025