Forrester: AI Agents Are Ready, People and Data Are Not

Forrester: AI Agents Are Ready, People and Data Are Not

The key technological components for truly autonomous AI are being assembled, but user mistrust in AI and missing usable data hamper adoption. In a report published on July 8, 2025, Forrester analysts detailed how the critical technology components needed for agentic AI applications to “act on behalf of an enterprise or individual, perform tasks, make decisions, and interact with data or other systems autonomously” are coming together. However, the report also highlights significant non-technical barriers to adoption.

Key Components for AI Agent Adoption

• Tool discovery and integration: Approaches like the Model Context Protocol (MCP) help AI agents discover and integrate various tools.
• Agent-to-agent interoperability: Protocols such as the Agent2Agent protocol (A2A) enable communication between AI agents.
• Orchestration capabilities: Systems that direct AI agents on what to do while providing interfaces for human users.

Adoption Barriers

Forrester identifies several challenges that slow AI agent uptake:
• Low trust in AI outputs: Both employees and consumers remain wary of AI decisions and recommendations.
• Misaligned workflows and missing data: Many organizations lack the necessary clean, accessible data to fuel AI agents effectively.
• Unclear and fragmented regulatory guidance: This creates uncertainty around compliance and risk.
• Workforce training gaps: Employees need more than just tool training; they require support to overcome fear, uncertainty, and doubt about AI potentially displacing them.
Stephanie Liu, Forrester senior analyst and report co-author, emphasized, “You have to ensure you're bringing employees on the journey. It's not just the training on how to use the tool, but helping them get over the fear, uncertainty and doubt of learning to use that which may outsource or displace them.”

AI Agents: The Technology and Use Cases

Thanks to large language models, AI agents can "reason" and determine the next best step in workflows, using third-party tools and data to recreate or optimize processes. Unlike traditional automation like robotic process automation (RPA), which requires humans to define every step, AI agents can learn and adapt workflows autonomously. Liu notes, “Formal documentation doesn’t always reflect the actual ways people do tasks. In the future, AI agents will figure out on their own the most effective, efficient way of getting a process done, which means people don’t have to document everything and formulate a ‘perfect’ workflow.” The report highlights different AI agent use cases, including consumer engagement, employee support, and enterprise automation, each progressing at varying rates. The technology is evolving from assistant- or copilot-style applications that provide summarization and writing help toward “solver agents” that autonomously perform tasks on behalf of humans or organizations.

Recommendations for Organizations

Liu recommends organizations start by clearly defining what they want their AI agents to do and identifying the data those agents need to access. “If you can't go down to the individual data sets or data sources, then you haven't scoped it properly,” she said. “Start small. Give it one step in a workflow and expand from there. Experimenting early sets you up to build a roadmap of what the next iteration of your AI agent will be.”

FAQ

What key technologies support AI agent adoption?

• Tool discovery and integration
• Agent-to-agent interoperability
• Orchestration capabilities

What are the main barriers to AI agent adoption?

• Low trust in AI outputs
• Misaligned workflows and missing data
• Unclear regulatory guidance
• Workforce training gaps

How do AI agents differ from traditional automation like RPA?

AI agents use large language models to autonomously learn and adapt workflows, whereas RPA requires human-defined steps.

Crypto Market's Take

The evolving AI agent landscape discussed by Forrester is well in tune with Crypto Market's offerings. Our platform leverages AI-driven solutions to provide automated trading tactics and risk management, similar to the "solver agents" highlighted in the report. With a rich array of tools in our AI Tools Hub, businesses can overcome adoption barriers by seamlessly integrating AI agents into their operations.

More to Read:

• How Agentic AI Broke the Rules of Martech Decisioning
• Walmart Bets on AI Super Agents to Boost E-Commerce Growth
• AI Crypto Coins Drive 2025 Innovation as Blockchain and AI Converge

Source: Originally published at No Jitter on July 25, 2025.