The Age of Agents - Part 1: What They Are and Why They Matter

Before diving in it is essential to build a clear understanding of what an agent is, and the different ways agents can be structured to solve business problems.

An agent is an AI system - typically driven by a large language model (LLM) - that can:

1. Reason through complex or ambiguous inputs
2. Decide on actions to take
3. Use external tools or APIs to complete tasks
4. Execute workflows autonomously, often across multiple steps

Because AI agents can perceive their environment, make decisions, and take actions to achieve specific goals, you can also think of them as ‘digital employees’ tackling tasks like data gathering, analysis, customer interaction, process automation, and decision support.

They have the potential to operate autonomously, executing decisions and actions without human intervention at every step. However, in our hands-on experience, fully autonomous agents, despite their power, remain too brittle and immature for most business-critical workflows. The problem heren is a lack of deterministic guarantees. LLMs are inherently probabilistic. Even with the exact same prompt and context, you may get different outputs on different runs. That variability makes it very hard to certify an agent’s behaviour in finance, healthcare, or other sensitive domains. As a result, hybrid or rule-based systems, where coordination relies on software logic or predefined rules, are more reliable in enterprise settings. In practice, AI today is used mainly for augmentation - enhancing human capabilities - rather than full automation.

Agents rarely work alone. They collaborate through structured, agentic workflows that define how tasks are delegated, coordinated, and completed across multiple agents and systems. When triggered—by a user request, a scheduled job, or an event—an agentic workflow guides an agent to:

• Interpret context and intent
• Access the right data or tools
• Hand off subtasks to specialized agents
• Determine next steps
• Return a verified result or execute a secure action

This modular approach is the operational backbone of agentic systems, keeping them auditable, extendable, and safe.

To be effective, workflows must be:

• Composable: Built from reusable steps like context gathering, tool invocation, and validation
• Context-Aware: Leveraging both static business rules and dynamic session data
• Guarded: Enforcing boundaries around data access, permissible actions, and fallback behavior
• Observable: Emitting structured logs for traceability and continuous improvement
• Business-Aligned: Tailored to real-world logic and compliance needs

Because business processes themselves are workflows of interconnected steps, agentic workflows can mirror human operations end to end. Consider HR onboarding: once a candidate accepts an offer, one agent verifies the contract, another extracts and stores personal details, a third schedules training and equipment delivery, and a fourth provisions system access - while HR specialists oversee the flow without handling every routine task. By delegating well-defined tasks to AI agents, companies empower employees to focus on strategic challenges that demand deeper judgment and creativity.

Well-designed agents show real promise in complex domains like HR casework, payroll, and customer support. Unlike static chatbots or brittle scripts, they adapt in real time to unstructured tasks.

For instance, an agent connected to enterprise systems can gather and reason across fragmented information - pulling from customer profiles, policy documents, or case histories - to generate more complete answers. It can also trigger actions through tool integrations, such as making API calls into HR systems to update records or retrieve payroll details, thereby automating parts of end-to-end workflows, and support service teams by surfacing the right knowledge at the right moment, all with minimal manual intervention.

AI agents can also function as always-on co-pilots that unify your knowledge base, instantly answer employee questions, and accelerates onboarding, while simultaneously tailoring every customer interaction in real time and operating around the clock to monitor live data, make decisions, and trigger actions the moment conditions change - true round-the-clock scale in one system.

In our own pilot projects at Faktion, this approach has already translated into measurable gains: for a major syndic company we built, an AI agent now handles 50 weekly reports with around 80% less manual effort, freeing up an entire workday each week, while Pronect’s security teams have slashed compliance‑gap analysis from three days to under four hours using our custom-built agentic system.

But agents aren’t silver bullets. Despite their sweeping promises, labour-market studies still find only modest real-world impact. LLM-driven systems can be unpredictable, and lack the transparent decision paths of traditional software. Integration with legacy tools can fail in subtle ways, and over time, model drift can introduce deviations that are hard to detect. These technical risks are compounded by broader concerns around compliance, fairness, and explainability - especially in sensitive domains like HR or finance.

That’s why some scenarios still favour traditional automation. Deterministic systems are better suited for tasks where consistency, auditability, and regulatory clarity are non-negotiable. Even in knowledge-intensive environments like our Ferranti MECOMS 365 assistant, we’ve learned that when answer confidence drops below a certain threshold, it’s better to escalate to a human expert than to guess. At Ferranti, that safety net is built into our system.

A subtler risk is the illusion of control. Agents that sound confident may hallucinate facts or reasoning chains. Because LLMs optimise for fluency, not truth, their self-explanations can be invented. Convincing answers can hide errors, and agents might act on these hallucinations, triggering real-world consequences. Remember: they are trained on data we can’t fully trace, so behaviors aren’t always predictable.

Ultimately, it’s not about choosing agents over rules, but knowing where each fits. While more and more voices point to the imaginative aspects of agents, experienced engineers will tell you that repeatable design patterns or a production-ready pipeline is very difficult to achieve.

To succeed:

1. Start small with a focused use case
2. Build in guardrails and keep humans in the loop
3. Validate impact before scaling
4. Rely on hard-won engineering patterns and governance

At Faktion, we combine LLM research, MLOps, modular agent components, deep integration expertise, and a governance toolkit that embeds monitoring, security, and compliance from day one. We bring the blueprint—and the hands—to turn agent hype into production reality.

Welcome to the Age of Agents. In this blog series, we’ll strip away the buzzwords and pull back the curtain on how these systems truly operate. We will pin down what agents are, and walk you through the engineering process step by step. Each post unpacks a different real-world scenario from Faktion’s playbook, tracing every decision, pitfall, and fix. If you’re tired of hype and want actionable insights and post-mortems you can apply tomorrow, you’re in the right place.

In the next episode we will move from ‘what agents are’ to the question that really matters: should you build one at all? In Part 2 we’ll lay out clear tests for when agents shine, the warning signs that call for other approaches, and a quick decision checklist you can reuse before writing a single line of code. See you in the next episode.