Agentic AI frameworks are ecosystems of autonomous agents that can observe their environment, consider goals, plan complex workflows, and act with minimal human input. Unlike traditional automation or generative tools, an AI agent pursues outcomes rather than just outputs. It combines large language models, reasoning, memory, and tool use across APIs, apps, and data services. Leading analysts identify agentic AI systems as a top strategic trend for 2025 because they can behave like virtual coworkers. They are powerful yet still require governance and control.
Why now? Costs have collapsed while capabilities have matured. The Stanford AI Index 2025 shows that the cost to query at GPT-3.5-level performance fell more than 280× from 2022 to 2024, making agentic AI solutions economically feasible across far more workloads.
Why agentic AI frameworks matter for large organizations
Agents can take on complex tasks that once demanded extensive code and constant human orchestration. Increasingly, multiagent systems let multiple agents coordinate agent workflows, sharing context and validation.
- In financial services, agents work together to detect fraud and support surveillance.
- In insurance, a single agent can manage claims intake while specialized agents process documentation or payments.
- In life sciences, they can iterate experiments or streamline regulatory documentation.
- In the public sector, agents can accelerate research, case triage, and service delivery — often with a human in the loop for oversight.
Multiple trackers show the AI-agents category expanding from roughly $5B in 2024 to over $47B by 2030. The implication for data science and IT leaders is clear: the shift to agentic AI is not a minor UX upgrade but a new operating model. Of course, as autonomy increases, identity, authorization, observability, and auditability become more essential. Security leaders now frame this as “identity security for non-human identities,” bringing AI agents under the same governance as employees and services.
How to evaluate agentic AI frameworks
Agentic AI frameworks aren’t turnkey apps. They’re the developer tools and runtimes that coordinate how an AI agent (or multiple agents) reason, plan, and act at scale. Think of them as the operating layer for your agentic system: they connect large language models, tools/APIs, data sources, and policies so agent workflows and multiagent systems actually work in production. All the way from customer support triage to regulated reporting.
They typically appear as: SDKs/libraries to build agents, memory, and orchestration logic; platforms (hosted or self-managed) that provide runtime, governance, and scaling; middleware/orchestration layers for inter-agent communication, state, safety, and routing; and composable frameworks that let you plug in your own models, tools, identity, and monitoring. These agentic AI solutions are foundations you build on rather than finished apps.
Evaluate beyond raw model accuracy
- Memory, reasoning, orchestration: Support for multistep planning, persistent context, and multiagent coordination on complex workflows and each complex task.
- Security and governance: Lifecycle oversight for every autonomous agent including discovery, least-privilege access, secrets, approvals, and policy enforcement with human-in-the-loop for high-impact actions.
- Access control and compliance: Centralized authorization aligned to the NIST AI Risk Management Framework (RMF) and enterprise risk controls.
- Observability and audit trails: Telemetry for every tool call, input/output, decision path, cost, and latency. This is essential in regulated AI systems.
- Integration: Clean connectors to enterprise data, apps, and workflows, as well as support for the Model Context Protocol for interop.
- Cost and performance: Clear benchmarks and autoscaling with visibility into cost-per-task and SLO/SLA conformance.
- Portability: Hybrid/multicloud options to avoid lock-in and keep skills close to data.
- Extensibility: Supports plugins, evaluators, red-teaming, and guardrails. Specialized agents or a single agent pattern are easily added where appropriate.
Bottom line: the best agentic AI framework is the one that balances power with oversight, integrates cleanly with your stack, proves cost and reliability at your expected load, and scales responsibly as your teams and your agents grow.
Current leading frameworks
The ecosystem of agentic AI framework solutions is evolving rapidly. Four widely used options include:
- AutoGen: Strong for conversational and task-driven multiagent systems, with flexible coordination and memory patterns.
- CrewAI: Designed for team-based agent workflows, offering clear role assignment and collaborative task decomposition.
- LangChain: Widely adopted for data and tool integration, with rich connectors and agentic abstractions for orchestration.
- LangGraph: Employs graph-based structures to manage complex workflows and inter-agent dependencies, supporting modular enterprise design.
Each AI framework emphasizes different strengths across reasoning, memory, and orchestration. The optimal choice depends on your organization’s priorities for security, traceability, integration depth, and scale.
Note that the frameworks focus on the agent runtime layer with reasoning, orchestration, and communication. An enterprise AI platform does that and significantly more: it adds end-to-end lifecycle (data access, development, deployment), built-in governance, observability, compliance, and reproducibility so agentic capabilities run securely at scale.
Architecture patterns for scale and ROI
Successful agentic systems balance broad reach with deep domain capability to “help everywhere” and “help that’s measurably right.”
- Horizontal copilots span teams and tools such as IT operations and customer support on shared infrastructure. One AI framework supports many use cases with reusable agent workflows, common guardrails, and pooled observability. These often use multiagent systems where multiple agents (retrieve, act, review) hand off context; a single agent works for a well-bounded complex task.
- Vertical workflows go deep in domains like underwriting or research analysis. They favor specialized agents with embedded policies, tighter SLO/SLA targets, lineage, and evidence capture, producing clearer cost-per-task and quality gains.
Most architectures blend both: horizontal layers provide auth, routing, evaluation, and monitoring; verticals add domain logic and compliance. To keep cost predictable and change manageable:
- Use open interfaces so agents can switch models/skills without rewrites.
- Run hybrid or multicloud to keep skills near data and avoid lock-in.
- Adopt identity-first execution so autonomous agents inherit least-privilege access control and policy.
- Standardize context passing (for example, Model Context Protocol) to reduce glue code.
- Benchmark end-to-end including latency, success rate, cost-per-completed task, and human-in-the-loop effort.
Design for portability and governance first; ROI follows from safer reuse, faster iteration, and lower operating variance.
Risk and governance considerations
Adopting agentic AI frameworks responsibly means embedding risk controls early and making them part of day-one architecture, not afterthoughts. Start with these focus areas:
- Agent sprawl: Prevent uncontrolled creation through centralized discovery and approval.
- Security risks: Treat every agent as a non-human identity with secrets management and behavioral monitoring.
- Bias and reliability: Apply pre-deployment testing and continuous evaluation aligned with NIST AI RMF standards.
- Observability gaps: Enforce lineage tracking across all agent workflows to preserve transparency.
NIST’s AI RMF and its Generative AI Profile remain the foundation for aligning design, deployment, and operations with traceability and accountability in a responsible governance program. Every AI agent handling sensitive actions should route through human-in-the-loop checkpoints and operate under strict budgets, rate limits, and safety constraints.
A practical evaluation scorecard
When comparing frameworks, use a consistent set criteria to measure readiness and fit. For example:
- Memory, reasoning, and orchestration support
- Integration with multiagent systems and the Model Context Protocol
- Governance, access control, and least-privilege design
- Observability, telemetry, and lineage tracking
- Security and secrets management
- Compliance with enterprise risk and audit policies
- Ease of embedding agent workflows in existing tools
- Cost efficiency and runtime scalability
- Bias, safety, and human-in-the-loop features
- Portability across hybrid and multicloud environments
A checklist helps standardize evaluation and ensures that cost, compliance, and control receive equal attention.
FAQs
What is an agentic AI framework and how does it differ from traditional AI assistants?
An agentic AI framework is the software environment where autonomous agents reason, plan, and act. Unlike traditional assistants that answer single prompts, agentic systems orchestrate multi-step workflows using large language models, memory, and tool/API calls. They can coordinate multiagent systems, maintain context across steps, and take actions in business systems with human-in-the-loop checkpoints and audit trails. In short, assistants respond; agentic frameworks execute goal-driven processes end-to-end, integrating data, policies, and controls so outcomes are reliable, governed, and repeatable in enterprise settings.
How do agentic AI frameworks improve enterprise workflows and automation?
They extend automation from scripted tasks to adaptive, outcome-driven work. Autonomous agents can triage customer support, reconcile financial data, or assemble regulatory filings by chaining reasoning, retrieving context, and calling tools across teams. Because frameworks standardize agent workflows, observability, and policy enforcement, organizations reduce manual handoffs, shorten cycle times, and improve consistency. Multiagent systems divide work among specialized agents while a single agent handles bounded tasks. Net result: faster throughput, fewer errors, better compliance evidence, and clearer ownership from request to resolution.
What factors should enterprises consider when choosing an agentic AI framework?
Prioritize capabilities that balance power with control: memory, reasoning, orchestration for complex workflows; lifecycle governance for each autonomous agent; granular access control; and deep observability. Verify the framework supports modular extensions, interoperability, and hybrid/multi-cloud portability. Assess integration with enterprise data and tools, secrets management, policy enforcement, and human-in-the-loop approvals. Demand cost and performance benchmarks such as latency and success rates along with security and compliance alignment to NIST AI RMF/ISO 42001. Choose the option that fits your stack, scales predictably, and delivers measurable ROI.
Are agentic AI frameworks secure enough for regulated industries?
Yes, but only when implemented with enterprise controls. Treat each AI agent as a managed identity with least-privilege access, secrets management, and continuous monitoring. Use frameworks aligned to NIST AI RMF and ISO/IEC 42001, enforcing policy gates, human-in-the-loop approvals, and full audit trails. Require telemetry for every tool call, dataset, and decision, plus lineage to reproduce results. Deploy in sovereign environments (on-premises, VPC, or multi-cloud) and integrate with existing IAM, DLP, and logging. With these safeguards, agentic systems meet regulated expectations for security, traceability, and accountability.
How can organizations measure the performance and ROI of agentic AI frameworks?
Track business impact and reliability together. Measure cost-per-task, cycle time, and quality versus baselines; attribute savings from reduced manual work and faster approvals. Monitor operational SLO/SLA metrics such as latency, success/error rates, and throughput to confirm scalability. Use observability to analyze agent workflows: where multiple agents collaborate, where a single agent suffices, and where human-in-the-loop intervention occurs. Tie all metrics to compliance evidence (lineage, audit logs). ROI emerges when outcomes improve, costs decline predictably, and controls remain intact across environments and workloads.
Getting started with enterprise agentic AI
Agentic AI frameworks are reshaping how enterprises execute work, turning static automation into adaptive, outcome-driven systems. The organizations that succeed implementing these frameworks will be those that balance autonomy with governance, scaling agents across teams while maintaining trust, control, and measurable ROI.
Domino Data Lab provides a developer-first, framework-agnostic workbench that lets teams use the languages, agent frameworks, and tooling they prefer while working in a single, reproducible environment. It combines unified access to enterprise data with built-in integrations to vector databases. By partnering with Domino, your AI teams can govern, trace, and operate at scale. Automatically record experiments, commits, configurations, and runtime interactions as a system of record and layer in embedded governance, an AI gateway, and granular access controls.
In addition, Domino exposes first-class orchestration primitives (configuration points, model/data hooks, and orchestration SDKs) and a cloud-agnostic runtime with automated infrastructure provisioning and one-click deployment. That enables teams to compose complex, multi-step agent workflows; run them consistently from code-first workspaces into hybrid production; and, scale agents without rewriting orchestration or infra logic.
Find out more in Omdia by Informa TechTarget’s complimentary report, "Key Considerations for Scaling Agentic AI." It explores recommendations, research insights, and platform capabilities for securely operationalizing agents across their lifecycle.