A Guide to Successfully Building Enterprise AI Agents
What is an AI Agent?
An AI agent is a sophisticated form of artificial intelligence capable of reasoning, taking action, and adapting in real time—either independently or with human guidance. Unlike simple, scripted chatbots, AI agents can orchestrate entire, multi-step workflows across your enterprise systems.
Across industries, a tectonic shift is underway in how teams get things done. According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. This explosive growth, however, highlights the critical need to avoid common pitfalls, as many initiatives are at risk of failure without the right foundation.
That’s why we’re seeing leaders across industries making strategic moves: Financial institutions are resolving compliance issues before regulators come calling. Utilities are automatically rerouting service teams in response to outages. Healthcare providers are coordinating patient care across multiple systems without the need for manual intervention.
Each case is a prime example of how agentic AI is changing work, streamlining operations, and improving productivity at scale. This guide outlines six critical elements to get right for a successful, enterprise-grade agentic AI implementation.
1. Choose a Strong Foundation: Salesforce vs. AWS
Every successful AI initiative starts with aligning technology to business priorities. For agentic AI, that means selecting the framework best suited to your core infrastructure, operating model, and team capabilities.
Most organizations looking for enterprise-grade AI agents choose either a full agentic AI platform like Salesforce Agentforce, or build on AWS infrastructure tools such as Amazon Bedrock AgentCore. Each approach offers robust security, scalability, and ecosystem integration, while enabling the speed and flexibility required for production-ready AI. But there are a few key differences.
Salesforce Agentforce
Agentforce is Salesforce’s native agentic AI platform, deeply integrated with CRM workflows, Data Cloud, and automation tools. It’s designed for rapid rollout, with prebuilt agents and low-code/no-code configuration that puts capabilities in the hands of business teams as well as developers.
AWS AgentCore
AWS AgentCore offers a more infrastructure-centric approach. It’s ideal for engineering-led deployments since it provides deep integration with AWS services, granular control over security and observability, and the flexibility to run custom logic and orchestrate multi-step, cross-system agents.
Agentforce is a natural fit for companies whose core workflows run inside Salesforce, while AgentCore is best for those building highly customized agents accessing sensitive data on AWS infrastructure. For some organizations, the choice is straightforward. For others, the best approach blends both — using Agentforce for Salesforce-native tasks and AWS AgentCore as the backbone for deep, cross-system automation.
2. Invest in a Strong Data Foundation
Agentic AI relies on accurate, relevant, and timely data. Without it, even the most advanced agent framework will deliver poor results. For this reason, organizations looking to activate AI are doubling down on data quality and connectivity.
This process typically starts with cleaning and unifying data to eliminate duplication, resolve conflicts, and ensure key attributes are correct. From there, organizations are implementing real-time data pipelines and using enterprise data platforms, such as Salesforce Data Cloud, to provide agents with rich, up-to-the-moment context.
3. Embrace RAG for Grounded Intelligence
What is RAG?
Retrieval-Augmented Generation (RAG) is a technique that combines generative AI with your own authoritative data to eliminate “hallucinations.” Before generating a response, the AI retrieves relevant information from your internal knowledge bases or other trusted sources, ensuring the output is accurate and brand-safe.
One of the fastest ways to lose trust — whether with employees, partners, or customers — is for AI to deliver a wrong answer with absolute certainty. Early generative AI tools were prone to hallucinations, making them risky for enterprise use. Retrieval-Augmented Generation (RAG) addresses this problem by combining generative AI with authoritative data from your own systems. Before producing a response, the AI retrieves relevant information from your knowledge bases, CRM, or other trusted sources, ensuring outputs are accurate and brand-safe.
4. Redesign Workflows Around Goals, Not Steps
Most traditional workflows follow a defined sequence of steps, regardless of context. Agentic AI works differently, pursuing outcomes rather than simply completing a checklist.
This shift requires leaders to rethink process design to map user intents directly to the desired result. Agents can then choose the optimal path based on real-time inputs, making processes more flexible and scalable.
5. Ensure Trust and Governance in AI Agent Deployments
Handing over tasks to AI doesn’t mean giving them complete autonomy — human supervision and governance are key. Enterprises must embed security, transparency, and explainability at every level of their AI architecture.
This means setting clear parameters for what actions agents can take, under which conditions, and with what human oversight. It also means equipping agents with explainability tools, so users can see why a decision was made, and continuously monitoring models for drift or performance issues.
6. Partner Strategically for a Successful AI Agent Implementation
Transitioning to agentic AI involves much more than a technology upgrade. Most organizations don’t have all the necessary expertise in-house to design, deploy, and optimize these systems end-to-end. So, a strategic partnership is crucial.
Forward-looking enterprises are partnering with experts who understand business use cases, data infrastructure, and the nuances of tool capabilities in Salesforce and AWS. At AllCloud, we help companies connect the dots between business strategy, infrastructure, and execution — whether that means launching a full Agentforce rollout, integrating AWS AgentCore for complex cross-system automation, or designing a hybrid approach that does both.
The Future Belongs to Those Preparing for Agentic AI
Agentic AI is rapidly becoming the backbone of modern enterprise workflows that streamline operations and improve productivity. Organizations that start preparing today will be ready to overcome challenges and lead tomorrow.