Unleashing Intelligent Automation: A Practical Guide to Building AI Agents

The landscape of software is evolving. We've moved from manual processes to automated workflows, and now, a new frontier is emerging: AI agents.

These systems, powered by the advancements in Large Language Models (LLMs), are capable of independently accomplishing tasks on our behalf with a high degree of independence.

This guide offers a practical look at how product and engineering teams can explore and build their first AI agents, drawing on insights from real-world deployments. This shift represents a fundamental rethinking of how intelligent systems can operate autonomously to accomplish complex tasks.

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What Exactly is an AI Agent?

Traditional software streamlines and automates existing workflows, but AI agents surpass this. They can perform these workflows on the user's behalf, exhibiting a significant degree of autonomy. Think of it this way: a conventional application might help you book a flight, but an AI agent could independently research the best options based on your preferences, handle the booking process, and even manage any unforeseen changes – all without constant user intervention. OpenAI defines them as solutions that can manage workflow execution, make decisions, recognise when tasks are complete, and transfer control back to humans when necessary.

Crucially, not every application that uses an LLM is an agent. A simple chatbot or a sentiment classifier, for instance, doesn't qualify. According to the guide, a true AI agent possesses core characteristics that enable it to act reliably and consistently:

• LLM-Powered Decision Making: It leverages an LLM to manage workflow execution and make decisions. It recognises when a workflow is complete and can proactively correct its actions if needed. In case of failure, it can also halt execution and hand control back to the user.
• Access to Tools: It has access to various tools to interact with external systems—both to gather context and to take actions—and dynamically selects the appropriate tools depending on the workflow’s current state, always operating within clearly defined guardrails.

What distinguishes agents from simpler AI applications is their ability to handle multi-step processes with a high degree of autonomy.

When Does Building an Agent Make Sense?

Building AI agents requires a fundamental shift in how systems make decisions and handle complexity. Unlike conventional automation, agents are uniquely suited to workflows where traditional deterministic and rule-based approaches fall short. Consider these scenarios:

• Complex Decision-Making: Workflows involving nuanced judgement, exceptions, or context-sensitive decisions, such as refund approvals in customer service workflows. Traditional rules engines might struggle with the subtleties involved, whereas an LLM agent can function more like a “seasoned investigator”, evaluating context and considering subtle patterns even when clear-cut rules aren’t violated.
• Difficult-to-Maintain Rules: Systems, such as vendor security reviews, have become unwieldy due to extensive and intricate rule sets, which can make updates costly or error-prone. Agents can simplify these processes by applying reasoning rather than following rigid scripts.
• Heavy Reliance on Unstructured Data: Scenarios that involve interpreting natural language, extracting meaning from documents, or interacting with users conversationally, for example, processing a home insurance claim. Insurance claims processing often involves sifting through emails, forms, and other unstructured content—a task where agents excel.

However, it's vital to validate that your use case genuinely meets these criteria before committing to building an agent. Otherwise, a simpler, deterministic solution might suffice. Prioritise workflows that have previously resisted automation, especially where traditional methods encounter friction.

The Foundational Building Blocks

In its most fundamental form, an agent consists of three core components:

• Model: LLM powers the agent’s reasoning and decision-making. Different models have different strengths and tradeoffs related to task complexity, latency, and cost. The guide suggests starting with the most capable model to establish a performance baseline. From there, you can try swapping in smaller models to see if they still achieve acceptable results and optimise for cost and latency. Not every task requires the smartest model; simpler tasks might be handled by smaller, faster models. Set up evaluations to establish a performance baseline and focus on meeting your accuracy target with the best models available.
• Tools: External functions or APIs the agent can use to take action. These tools extend your agent’s capabilities by using APIs from underlying applications or systems. Examples include tools for data retrieval (e.g., querying transaction databases or CRMs, reading PDF documents, or searching the web) and action execution (e.g., sending emails and texts, updating a CRM record, or handing off a customer service ticket to a human). Importantly, agents themselves can serve as tools for other agents – see the Manager Pattern in the Orchestration section. Each tool should have a standardised definition, enabling flexible, many-to-many relationships between tools and agents. As the number of required tools increases, consider splitting tasks across multiple agents.
• Instructions: Explicit guidelines and guardrails defining how the agent behaves. High-quality instructions are essential for any LLM-powered app, but especially critical for agents. Clear instructions reduce ambiguity and improve agent decision-making, resulting in smoother workflow execution and fewer errors. Best practices for agent instructions include
  • Use existing documents: When creating routines, use existing operating procedures, support scripts, or policy documents to create LLM-friendly routines.
  • Prompt agents to break down tasks: Providing smaller, clearer steps from dense resources helps minimise ambiguity.
  • Define clear actions: Make sure every step in your routine corresponds to a specific action or output.
  • Capture edge cases: A robust routine anticipates common variations and includes instructions on how to handle them with conditional steps or branches.

Orchestrating Intelligent Workflows

With the foundational components in place, you can consider orchestration patterns to enable your agent to execute workflows effectively. Customers generally find greater success with an incremental approach, despite the temptation to build a fully autonomous agent with a complex architecture right away. In general, orchestration patterns fall into two categories:

• Single-Agent Systems: A single model, equipped with appropriate tools and instructions, executes workflows in a loop. This approach keeps complexity manageable initially and simplifies evaluation and maintenance. Prompt templates can be an effective strategy for managing complexity within single-agent systems by allowing a single flexible base prompt to adapt to various use cases. Every orchestration approach needs the concept of a ‘run’, typically implemented as a loop that lets agents operate until an exit condition is reached. Common exit conditions include tool calls, a certain structured output, errors, or reaching a maximum number of turns. Our general recommendation is to maximise a single agent’s capabilities first. Splitting into multiple agents might be necessary when instructions become too complex or when there is significant overlap or unclarity in the available tools.
• Multi-Agent Systems: Multiple coordinated agents distribute workflow execution. We can model these as graphs, representing agents as nodes. Two common patterns emerge:
  • Manager Pattern: A central “manager”; agent coordinates multiple specialized agents via tool calls, each handling a specific task or domain. Instead of losing context or control, the manager intelligently delegates tasks to the right agent at the right time, effortlessly synthesising the results into a cohesive interaction. This pattern is ideal for workflows where you only want one agent to control workflow execution and have access to the user.
  • Decentralized Pattern: Multiple agents operate as peers, handing off tasks to one another based on their specialisations. Handoffs are a one-way transfer that allows an agent to delegate to another agent. This is optimal when you don’t need a single agent maintaining central control or synthesis—instead allowing each agent to take over execution and interact with the user as needed. This pattern is especially effective for scenarios like conversation triage.

The Agents SDK offers a code-first approach to orchestration, allowing developers to directly express workflow logic using familiar programming constructs without needing to pre-define the entire graph upfront. Some frameworks are declarative, requiring developers to explicitly define every branch, loop, and conditional in the workflow upfront through graphs. While beneficial for visual clarity, this approach can quickly become cumbersome.

The Critical Role of Guardrails

Guardrails are essential for ensuring that AI agents operate safely, predictably, and effectively. They act as a layered defense mechanism to manage data privacy risks and reputational risks. You can set up guardrails that address risks you’ve already identified for your use case and layer in additional ones as you uncover new vulnerabilities. Guardrails should be coupled with robust authentication and authorisation protocols, strict access controls, and standard software security measures. Think of guardrails as a layered defense mechanism; using multiple, specialised guardrails together creates more resilient agents.

Types of guardrails include:

• Relevance classifiers: Ensure agent responses stay within the intended scope by flagging off-topic queries.
• Safety classifiers: Detect unsafe inputs (jailbreaks or prompt injections) that attempt to exploit system vulnerabilities.
• PII filters: Prevent unnecessary exposure of personally identifiable information (PII) by vetting model output.
• Moderation: Flags harmful or inappropriate inputs (hate speech, harassment, violence).
• Tool safeguards: Assess the risk of each tool available to your agent by assigning a rating based on factors like access, reversibility, and impact.
• Rules-based protections: Simple deterministic measures (blocklists, input length limits, regex filters) to prevent known threats.
• Output validation: Ensures responses align with brand values via prompt engineering and content checks.

Building effective guardrails involves:

1. Focus on data privacy and content safety.
2. Add new guardrails based on real-world edge cases and failures you encounter.
3. Optimise for both security and user experience, tweaking your guardrails as your agent evolves.

The Agents SDK treats guardrails as first-class concepts, relying on optimistic execution by default. Under this approach, the primary agent proactively generates outputs while guardrails run concurrently, triggering exceptions if constraints are breached. Guardrails can be implemented as functions or agents that enforce policies.

Furthermore, planning for human intervention is a crucial safeguard. This allows for graceful transfer of control when an agent can’t complete a task or when high-risk actions are involved. Two primary triggers typically warrant human intervention: exceeding failure thresholds and high-risk actions. Human intervention is especially important early in deployment, helping identify failures, uncover edge cases, and establish a robust evaluation cycle.

Real-World Applications

Organisations across industries are already leveraging agents to transform their operations:

• Customer service agents that can resolve complex issues without human intervention.
• Research agents that gather, synthesise, and analyse information from multiple sources.
• Operational agents that coordinate logistics, manage inventory, or schedule resources.
• Development assistants that can write, test, and deploy code based on high-level requirements.

The Implementation Journey

If you're considering implementing agents in your organisation, an incremental approach is recommended:

1. Start with clear use cases that have measurable value and have resisted traditional automation.
2. Build a prototype with the most capable models to establish a performance baseline.
3. Iterate on tools and instructions to improve reliability and effectiveness.
4. Implement comprehensive guardrails to ensure safe operation.
5. Gradually expand capabilities as you validate performance in real-world scenarios.

Challenges and Considerations

Despite their promise, agents aren’t without challenges:

• Ensuring reliability in complex, open-ended tasks remains difficult.
• Balancing autonomy with control requires careful design.
• Managing costs associated with more sophisticated models.
• Addressing security and privacy concerns when agents have access to sensitive systems.
• Setting appropriate user expectations about agent capabilities and limitations.

The Future of Agent-Based Automation

We're just beginning to explore the potential of agent-based automation. As language models become more capable and development frameworks mature, we can expect agents to take on increasingly sophisticated tasks. The most exciting developments will likely come from agents that can effectively collaborate with humans—augmenting human capabilities rather than simply replacing them.

Conclusion

AI agents represent a paradigm shift in automation—moving from rigid, rule-based systems to flexible, reasoning-driven approaches that can handle ambiguity and complexity. The key to successful deployment lies in thoughtful implementation: choosing the right use cases, designing effective orchestration patterns, implementing robust guardrails, and taking an iterative approach to deployment. Start small, validate with real users, and grow capabilities over time. With the right foundations and an iterative approach, agents can deliver real business value—automating not just tasks, but entire workflows with intelligence and adaptability. The age of truly intelligent automation has only just begun.