Building Autonomous AI Agents

Successful AI agents are a matter of careful design, strategic decisions and continuous learning and adjustment. Cultivated with vision and care, such agents can revolutionize not only tasks, but workflows themselves, bestowing upon them unmatched intelligence and flexibility. This insight holds the future of enterprise productivity in the balance.

A new generation of digital presence is revolutionizing the very foundations of enterprise functionality: the AI agent. Ditch simple chatbots, these are something much smarter: something that, like an Olympic champion, becomes a master through not simply natural ability but through unyielding, deliberate practice. These are no mere tools; these are self-sufficient platforms that can comprehend context, formulate qualified decisions and carry out multi-step processes with an impressive level of autonomy.

In fact, the potential of AI agents goes well beyond process automation; they hold the power to enable levels of productivity never before seen and revolutionize the way that work is done in a wide range of disciplines, from software development through financial analysis. Yet, like any disruptive technology, their effective implementation will depend on a root understanding, an organizational vision, and a rigorously applied implementation strategy. It’s not a plug-and-play future, but one that will need wise engineering and a sharp understanding of the realities of operations.

Brain, Limbs and Guiding Principles

Fundamentally, an AI agent is a computer program that assists individuals by executing tasks and responding to queries based on a massive amount of language input that it learns from. What makes an agent distinct from, say, a clever spreadsheet macro, however, is the fact that it is capable of using a Large Language Model (LLM) to direct workflow execution, make real-time decisions and even self-correct or suspend execution in the event of an impasse. It’s exactly this ability to execute nuanced reasoning that makes agents so well-fitted to workflows long defying traditional, deterministic automation — those filled with rich decision-making, bogged down with cumbersome, hard-to-sustain rulesets or based heavily on extracting unstructured data.

Take, for example, the complex waltz of payments fraud analysis. A traditional rule-based engine works much akin to a strict checklist, marking down payments based on pre-configured rules. An LLM agent works differently – extracting nuanced patterns and sensing suspicious behavior in areas where hard-edged rules may provide no obvious guidance, applying smart, responsive judgment in difficult, messy situations. The design of such an agent is based on three interconnected pillars:

• The Model (brain): This is the LLM that drives the agent’s decision-making and reasoning functionality. Its selection is a determining factor, informing everything from cost to performance. One may be inclined to use the most capable model available to perform any task. Yet, much like an optimally managed investment portfolio, effective resource optimization requires that not every task requires the most capable and frequently most costly model. Although it is understandable to prototype using the most powerful LLM to set a performance bar, the strategic play is then to optimize cost and latency by substituting in smaller, quicker models where satisfactory results are still obtainable. Pre-trained models such as GPT or BERT provide a good starting point, having learned tremendous amounts of general linguistic knowledge, which a fine-tuning stage can further optimize to the particular idioms of your app.
  
  
• Tools (limbs): Lacking tools, an agent is a lone intellectual, unable to engage with the larger digital economy. Tools are outside functions or APIs that serve to extend the capabilities of the agent, enabling it to obtain context (e.g., query databases, search the web) or perform actions (e.g., send emails, update CRM records). It is a matter of standardization and reusability; good tools extend discoverability and ease version control, so your agent is not merely a brain, but a brain with good, functional limbs.
  
  
• Instructions (guiding principles): These are the precise guidelines and safeguards specifying how the agent acts. In the world of the agent, vague instructions are like unclear monetary policy — present uncertainty, result in less-than-optimal results and can cripple decision-making. Sound practice is to draw on established operating procedures, divide tasks into well-specified small steps and spell instructions out in detail. Importantly, sound instructions will pre-empt and cover edge cases and unanticipated input, just like a good legal framework preempts unforeseen circumstances.

The Regulator

Releasing an AI agent without solid guardrails is liable to be like releasing a financial product without a governing framework — rife with peril and a major reputational hazard. Guardrails are your bulwark, your multiple levels of protection, to ensure data privacy is never compromised, to reduce reputational risk, and to codify brand-consistent behavior. These protections exist in different types: relevance classifiers to ensure responses remain on-topic, safety classifiers to identify malicious “jailbreak” efforts, PII filters to avoid accidental disclosure of sensitive data, moderation tools against harmful messages and rule-based protections against identified threats.

Importantly, tool safeguards are vital; applying risk scores to tools (e.g., read versus write access, monetary impact) and capable of triggering automation checks or even human escalation of high-consequence actions.

But most important of all, perhaps, is the human intervention mechanism. It is not an indication of failure but a necessary safety measure. When an agent goes past pre-specified failure limits (e.g., repeated efforts trying to comprehend user intent) or encounters risky actions (e.g., approving a large refund), gracefully taking control away from the agent ensures business continuity and avoids possibly expensive blunders. It is a constant feedback cycle, fine-tuning the agent’s capabilities in actual environments while keeping essential control.

Managing Complexity

Once the building blocks are established, the next concern is orchestration: how will your agent handle workflows? The tempting reaction is to go ahead and build a large, convoluted multi-agent system. But experience teaches that an incremental strategy is often more satisfactory. One-model, single-agent system in which a single model, with desirable tools and instructions, runs workflows in a loop can be quite adequate and less overwhelming to begin with. Complexity can be dealt with by adding tools in an incremental manner and employing flexible prompt templates that can be tuned to different contexts, a bit like a flexible economic policy framework that can be adjusted without a total overhaul.

But with genuinely complex workflows — those weighed down by an excessive amount of overlapping tools or complex conditional logic that renders a single prompt cumbersome — the time will come to look towards a multi-agent system. In this case, execution is divided amongst planned agents, much like a specialist economic system where various entities perform different functions. Two trends are observed:

• The Manager Pattern: A single central “manager” agent coordinates specialist agents by invoking tools. It keeps control and context while intelligently delegating and compiling results into a coherent conversation. It’s similar to a central bank working with multiple financial institutions towards a greater economic objective.
  
  
• Decentralized Pattern: Instead, agents are peers and exchange workflow execution directly with each other based on specialization. This is best when no agent must hold any central control, enabling full ownership by each one and direct communication with the user when necessary. Consider a network of dispersed specialty firms, each working on a separate segment of a supply chain.

The Iterative Process

Developing an AI agent is an ongoing, iterative process of training, assessment and fine-tuning. It commences with careful preparation and collection of data — for example, obtaining text transcriptions, voice recordings and interactions and subsequently cleansing and labeling this data so the agent can receive proper learning material.

Once in place, the agent will need to be extensively tested and validated, akin to a new economic model put through empirical examination. This includes unit tests on separate components, user tests in controlled environments and A/B tests on different iterations in search of maximum performance. Perhaps most importantly, vigilance against overfitting —  where an agent will perform spectacularly well on its training set but struggle with new, unseen data, like an economic theory that perfectly describes the past but is useless at predicting the future — is a prerequisite. Constant feedback loops, derived from user interactions and metrics, are necessary to refine continuously.

The final step, deployment is but the first step of a sustained period of observation and adjustment. The agent will need to be inserted into its real-world environment and monitored with careful attention to ensure the agent deploys well and also learns and fine-tunes over time, consistently addressing evolving user requirements and expectations.

—Here’s a great primer on how to get started on creating your first AI agent.
Additional sources: OpenAI, SalesForce