Building AI Agents: 6 Tips for Success

AI has evolved rapidly—from basic algorithms that suggested content to generative models that create it. Now, we're entering the AI agent era.

AI agents refer to sophisticated AI systems that use reasoning and iterative problem solving to achieve specific goals. Instead of waiting for instructions, they adapt and take initiative.

Agentic AI has transformative potential for enterprises. Procurement teams can use AI agents to draft contracts, cross-check regulatory compliance, and determine if the contract may be protested. IT teams can deploy agents that orchestrate IT systems, identify outage root causes, and resolve issues before employees notice.

However, implementation requires navigating technical and strategic considerations. Below, we explore six essential factors for building effective AI agents.

High-performance AI agents require strong foundation models for success. But selecting the right foundation models isn’t always straightforward. Consider choosing between Anthropic's Claude models. The most capable option—Claude 3.7 Sonnet—excels at complex reasoning tasks. But situations requiring rapid responses might work better with Claude 3.5 Haiku. That model offers faster processing and lower costs while maintaining strong performance on straightforward text-based tasks.

Foundation model capabilities are evolving rapidly as vendors race to meet industry benchmarks. Evaluation frameworks like MMLU, GPQA, and GSM8K provide standardized benchmarks for models. But research shows that businesses must consider latency requirements, reasoning depth, and multimodal capabilities when choosing the right FM.

Selection is nuanced for enterprise applications. Analyzing dense documents may justify high compute costs, but customer-facing apps with tight latency demands benefit from lighter, specialized models. Thes solution is to use model-switching architectures. These let agents pick the best model for each task—balancing speed, cost, and capability.

Tools extend agent capabilities by enabling them to analyze documents, query databases, or interact with external systems. Think of AI tools like apps on your smartphone. Your phone can do basic things on its own (making calls or sending texts), but apps extend the phone’s functionality.

Without tools, AI agents can only chat and answer based on what they know. With them, they can check calendars, send emails, look things up, or control other software.

A strong set of tools enables smooth integration within enterpise processes and systems. For example, an AI agent can use separate tools to pull data from a CRM, analyze trends, and trigger a marketing action on its own.

Most agent frameworks (think LangChain or AutoGPT) make this happen via a function-calling interface. Each tool is described with:

1. Name and purpose—what it is and why you’d use it
2. Inputs—what parameters it needs
3. Outputs—what it returns

These turn AI agents into team members that plug into your systems and make decisions that drive real business value.

Effective agentic AI processes require thoughtful orchestration between attended workflows (direct human collaboration) and unattended workflows (autonomous operation within established boundaries). 

Attended workflows are best for tasks requiring human judgment at key decision points. In these scenarios, AI adds value through conversational interfaces—enabling users to query complex systems in natural language and receive real-time, context-aware guidance.

Unattended workflows boost efficiency and throughput, but require guardrails to manage risk and ensure consistent performance.

For example, a mortgage agent could automatically process straightforward applications—pulling missing data and applying policy rules—then route complex cases to human underwriters. 

Embedding AI agents in structured processes allows you to audit, monitor, and control critical decision points—striking the right balance between automation and oversight.

Multi-agent collaboration automates complex enterprise workflows through specialized agents working in concert. Each agent handles distinct responsibilities while sharing information and coordinating efforts. These agent networks mirror organizational structures. Individual agents act like employees, focusing on domain-specific tasks and collaborating with others. 

For example, in a supply chain, inventory agents track stock and demand, logistics agents optimize routes and costs, and procurement agents manage vendors and contracts.

Dividing tasks between agents has big advantages: easier training, simpler design, and clearer accountability. The approach also enables tighter control of sensitive tasks, with governance applied at each collaboration point. 

But multi-agent systems need advanced coordination. Agents must communicate effectively and reconcile conflicting objectives. The most effective setups use automation platforms with standardized protocols, state management, and integration tools. 

AI agents require memory systems to process and recall information. Let's break down two essential memory types every effective agent needs:

Short-term memory (STM) is the agent's working memory during interactions. STM maintains conversation context, tracks ongoing tasks, and holds immediately relevant information. The best implementations use conversation buffers to retain recent exchanges and state management systems to track tasks and progress.

Long-term memory (LTM) stores persistent information across sessions and users. Implementing LTM typically involves vector databases for semantic search, traditional databases for structured data, and strategic fine-tuning to adapt agent behavior over time. Agents then use historical patterns to recognize preferences and improve their predictions.

For example, a procurement agent with long-term memory can identify contracts likely to face protests by recognizing patterns across hundreds of past cases—something short-term recall can’t do.

In regulated environments, some organizations prefer run-time data retrieval of stored system prompts as an LTM architecture to enforce privacy and governance. Leveraging both memory types within a robust governance framework ensures your AI agents remain adaptive and trustworthy.

Auditability and transparency are critical for AI agents in regulated industries and complex workflows. These systems must not only deliver accurate results but also explain every action. Traceability—the ability to track actions, reasoning, and decisions throughout an AI agent’s workflow—helps catch errors early and limit their impact.

Because organizations value data privacy, many have hesitated to deploy AI to critical processes. But the right platform makes this a non-issue. They provide governance, keep information private and secure, and never use your data to train their own models.

Platforms offering the latest foundation models through a secure, fully managed system—along with event history and real-time monitoring—make traceability and privacy possible. This ensures agent actions can be tracked, trusted, and audited with confidence.

AI agents fundamentally reshape how work gets done. Process platforms allow you to integrate these agents directly into mission-critical processes, removing the complexity of configuring each individual component. Plus, process platforms enable built-in governance and simplify model selection. And by putting them in your critical processes while maintaining safety, they can provide key business value and deliver measurable results like time saved or improved task accuracy. 

Keep these six tips in mind. They're key to successful agentic AI usage.