Over the past six months it feels as though every tech headline has featured a new AI agent demo: a chatbot that books flights end-to-end, a digital analyst that churns out board-level research in minutes, or a warehouse bot that re-plans delivery routes on the fly. Despite the hype, these aren't science-fiction prototypes — they're production systems already saving businesses real money.
In this article:
- What's already live (non-technical tour)
- Five repeatable architecture patterns
- What makes it work: data decisions that matter
- Where agents deliver the most value
- How to get started — and what to watch out for
- SaaS vs. Custom Development: Why Code Wins
- Understanding AI Agent Limitations
- Final thoughts: agents are not the future — they're now
- Examples of AI Projects We've Developed
- Glossary of Terms
Now, let's get started with the basics.
What makes an AI agent different from last year's chatbots? It comes down to four main things:
- Planning & Autonomy – the model decides what to do next instead of waiting for a human menu click.
- Tool Use – it calls live APIs (software interfaces that connect different systems), databases, or even a browser to gather facts and execute tasks.
- Memory – it keeps track of context so conversations and workflows don't reset every time you type.
- Multi-step Reasoning – Breaks down complex tasks into smaller steps, completing each one before moving to the next (like a human would), rather than giving just one simple response.
These four things fundamentally change what the AI is capable of, and therefore the breadth of ways you can deploy them in your business.
Give an agent the right tools and a clear goal and it will plan → call tools → observe → re-plan until the job is done, escalating only when human judgement is truly needed. The result? 24-hour support desks, self-healing IT systems, and delivery fleets that adapt to storms automatically. In other words, agents turn yesterday's "nice-to-have chatbot" into a hands-on digital colleague.
In the sections that follow we'll show real deployments from customer service, finance, healthcare, logistics, education and more — plus the repeatable patterns you can copy to build your own.
New to AI agents? If you see a term you don't recognise, don't worry — there's a glossary of terms at the end of this article. We included it to help you quickly get up to speed on the jargon and concepts that come up throughout the guide, as we necessarily touch on some technical topics.
1. What's already live (non-technical tour)
Let's look at where AI agents are already in action — not lab experiments, but real deployments driving ROI in 2025. We've summarised the most impactful examples across industries, with a focus on the outcome they delivered.
| Domain | Real-world Example | Quick Win It Delivered |
|---|---|---|
| Customer Service | Klarna's LangChain-powered assistant (AI customer-service agent) | Handles 85 million users' queries, resolving issues 80 % faster |
| Internal IT / HR | Moveworks AI helpdesk (GenAI employee-support platform) | Reduces internal support calls by 44 %; saves 60 000+ staff-hours / month |
| Software Delivery | Amazon Q Developer Agent (autonomous coding & PR assistant) | Writes, tests, and opens PRs autonomously — reduces dev time and bugs |
| Cyber Security | Microsoft Security Copilot (LLM-powered SOC investigator) | Auto-investigates phishing & identity threats; slashes incident-response times |
| Logistics | DHL Routing Agents (AI parcel-routing system) | Adaptive delivery planning boosts hub throughput by 40 % |
| Finance | Numerai DANCR Pipeline (self-driving quant-research agent) | Autonomously ideates and runs trading strategies, 24/7 |
| Healthcare | TU Dresden Oncology Agent (clinical decision-support AI) | Delivers 91 % correct treatment plans using imaging + genomics |
| Enterprise Admin | Salesforce Agentforce for Health (insurance & EHR automation agent) | Automates insurance verification and EHR logging |
| Education | Khanmigo AI tutor (adaptive learning assistant) | Delivers personalised maths coaching globally with adaptive pedagogy |
| Gaming & Media | NVIDIA ACE NPCs in PUBG (generative AI non-player characters) | NPCs that talk, plan, and squad-up like human teammates |
These examples are live, running, and creating business impact — showing how AI agents are already evolving from one-off demos into reliable digital workers.
2. Five repeatable architecture patterns
Under the hood, most successful AI agents follow a few clear structural patterns. These help teams coordinate complex behaviour, reduce risk, and build agents that scale. Whether you're solving support tickets or optimising deliveries, the following setups have proven highly effective.
Understanding the different ways that AI agents can be configured and interconnected helps you identify which problems they can solve, thanks to the benefits of those structured environments. For example, research papers show there’s a balance between how much complexity you give an agent and when you should split it into sub-agents. If you put too much complexity into a single agent, its performance can start to degrade. Splitting tasks using these agent patterns, and enabling agents to hand off tasks between each other, can be an effective way to increase performance.
1. Router → Specialist Sub-agents
This pattern uses a "traffic controller" agent that analyses the user's request and routes it to one of several domain-specific agents. Each sub-agent handles a narrow task (like returns, payments, or order status).
Example: Klarna's support assistant classifies customer intent and delegates to dedicated agents trained for each category, speeding up resolution and reducing confusion.
2. Planner + Parallel Workers
In this setup, a central planner agent breaks down a big problem into smaller ones and delegates these to a team of helper agents that work in parallel. Once the workers return their results, the planner consolidates the outcome.
Example: Anthropic's research mode spins up multiple Claude agents to fact-check or summarise different sources simultaneously, then merges the findings into a single report.
3. Single Agent + Skill Plug-ins
A single powerful agent is paired with a suite of callable tools (or "skills") exposed through code. These can include internal APIs, file systems, databases, or even compilers — anything the agent can invoke programmatically.
Example: Amazon Q Developer uses plugins to run unit tests, check repo files, and open pull requests, all from a single conversation window.
4. Perceive → Plan → Act Loop
For real-time or dynamic environments, agents use a continual loop: observe the world, reason about the best next step, and act accordingly. Then repeat. This allows them to adapt to changes on the fly.
Example: DHL's delivery optimisation agents re-plan routing when they detect delays. NVIDIA's game characters also follow this loop to respond naturally in gameplay.
5. Supervisor / Pedagogy Monitor
In sensitive domains like education or healthcare, a second agent is tasked with oversight. It can steer or overrule the main agent if it detects errors, bias, or user confusion.
Example: Khan Academy's Khanmigo uses a supervisory agent to track learning goals and monitor the tone and pacing of the tutoring conversation, ensuring alignment with pedagogical best practices.
These structures are composable — you can start with one and evolve toward multi-agent systems as complexity grows. The key is to think in terms of roles and responsibilities: planner, executor, critic, and router. That way, your system remains interpretable and scalable.
6. And many more patterns too
Under the hood, most successful AI agents follow a few clear structural patterns, each designed to solve particular types of problems. Beyond the foundational setups described above, there are several more advanced agent architectures worth knowing about.
- The Self-Reflection (Critic) pattern has the agent critique its own output before finalising, greatly improving quality and reducing hallucinations.
- Blackboard (Shared-Memory) agents collaboratively build solutions by reading and writing to a central shared state, ideal for problems too complex for one agent alone.
- Meanwhile, Auction-based Allocation lets multiple agents rapidly self-organise by bidding on tasks, perfect for swarms and decentralised workloads.
- The ReAct (Reason-Act) approach explicitly alternates thinking and acting steps, ensuring transparency in decisions.
- Finally, the Evolutionary (Population Search) method involves generating many agent variants, iteratively selecting and improving upon the best performers—a powerful tool for open-ended optimisation tasks.
Importantly, these patterns aren't exclusive. In fact, the most robust and adaptable AI systems often arise from combining multiple approaches into a single unified architecture. For example, you might have an agent that employs ReAct reasoning within a Planner–Parallel Worker setup, with an additional Self-Reflection critic ensuring output quality.
Alternatively, Auction-based agents might coordinate via a Blackboard shared-memory system, each periodically evolving through Population Search. By mixing and matching these methods, you can tackle increasingly complex challenges, creating AI systems that scale elegantly with the complexity of your business problems.
3. What makes it work: data decisions that matter
No matter how clever your agents are, they're only as good as the data and tools they're connected to. Building robust, helpful agents means making the right decisions about what data to expose, how to structure it, and where to draw the line between autonomy and oversight.
🔌 Tooling vs Raw Data
One of the biggest decisions is whether to let your agents call APIs (structured tooling), or work from unstructured data like PDFs, spreadsheets, or web pages.
- Tools = safer and more deterministic. You know what happens when the agent clicks "Cancel Booking".
- Data = richer and more flexible. Great for research, discovery, or open-ended dialogue.
Many of the strongest systems combine both. For example, an agent might read your product catalogue (data) and then place an order via your backend API (tool).
🔐 Access and Scope
Giving agents full access to your database or APIs sounds powerful — but it also increases risk. We recommend wrapping data behind "guardrails":
- Use permission layers (per-user or per-agent)
- Throttle sensitive actions (e.g. payments, cancellations) behind approvals or sanity checks
- Track what the agent saw and did to enable easy debugging
🧠 Memory vs History
Most useful agents need to remember things across interactions: what the customer said last week, or which draft you approved yesterday. This could be handled with:
- Context windows – short-term memory (like a person's working memory during a conversation - cheap but limited in how much information can be held at once)
- External memory – long-term structured recall via specialized databases that store and retrieve information (like a sophisticated filing system that can find relevant documents based on meaning, not just keywords)
Klarna's assistant, for example, doesn't just answer in the moment — it retrieves past conversation threads and order history to offer personalised help.
🔍 RAG and Vector Storage Strategies
One of the most powerful patterns for agent memory is Retrieval-Augmented Generation (RAG) — where agents don't just rely on their training data, but actively search through your organisation's knowledge base to find relevant context before responding.
The core concept is simple: convert your documents, conversations, and data into mathematical representations (called "embeddings"), store them in a specialized database, then let your agent search through them by meaning rather than exact word matching. Think of it like having a super-smart librarian who can find relevant information even when you don't know the exact words to search for.
🏗️ Implementation Approaches
- Naive RAG – Simple chunking and retrieval. Fast to set up but can struggle with complex queries that span multiple documents.
- Hierarchical RAG – Chunks documents at multiple levels (paragraph, section, document). Better for complex reasoning but more expensive to maintain.
- Hybrid RAG – Combines vector search with traditional keyword search and graph relationships. Most robust but requires more engineering.
- Agentic RAG – Uses multiple agents to plan queries, critique results, and synthesise findings. Powerful for research tasks but can be slow.
⚖️ Key Tradeoffs
- Freshness vs Performance – Real-time indexing keeps data current but increases response time (latency). Most teams batch updates daily or weekly to balance speed with accuracy.
- Chunk Size vs Precision – Smaller chunks (100-300 "tokens" or roughly 75-225 words) give precise retrieval but lose context. Larger chunks (1000+ tokens or roughly 750+ words) preserve context but may include irrelevant information.
- Retrieval Depth vs Cost – Retrieving 50+ documents gives comprehensive context but dramatically increases costs (since you pay per word processed). Most production systems retrieve 3-10 chunks to balance thoroughness with budget.
- Embedding Quality vs Speed – High-quality models like OpenAI's text-embedding-3-large produce better semantic understanding but are slower and more expensive than lightweight alternatives.
🎯 When RAG Works Best
RAG shines when your agents need to:
- Answer questions about internal policies, procedures, or documentation
- Reference historical conversations or case studies
- Provide context-specific recommendations based on past similar situations
- Stay current with frequently changing information (product specs, legal updates, etc.)
🚫 When to Skip RAG
RAG adds complexity and cost. Skip it when:
- Your use case is purely transactional (booking, payments, simple workflows)
- The required knowledge fits comfortably in the model's context window
- You need guaranteed consistency rather than nuanced understanding
- Real-time performance is critical and you can't afford retrieval latency
Pro tip: Start with a simple RAG implementation over your most frequently accessed documents. You can always add sophistication later, but getting basic semantic search working quickly will teach you what your agents really need to know.
🧱 Data format strategy
Agents thrive when you feed them clean, structured content. Even just creating JSON or Markdown wrappers around your docs or spreadsheets can dramatically improve results. Where possible:
- Use standard schemas (e.g. ISO dates, known key names)
- Make file and field names human-readable
- Label sensitive fields clearly (e.g. "Do not edit") in comments or metadata
The TL;DR? You don't need to clean your whole dataset. You just need to make the agent's starting point clean, predictable, and actionable. The rest it can learn to ask for.
The last point I want to cover briefly in this section is fine-tuning. If you have many examples of your inputs and the ideal outputs—whether those outputs are AI-generated or have been modified by a human to show best practice—you can use these samples to improve the model. Fine-tuning increases accuracy, reduces errors, or allows you to match a specific stylistic preference, such as a consistent tone of voice or a required document structure.
You can use different fine-tuning methods, such as telling the model which outputs you prefer, to optimise it for a particular use case. However, this is quite an advanced step, usually taken after you have already achieved good results with an initial, untuned model. Fine-tuning is more about squeezing extra performance from a model, rather than enabling something entirely new that was previously impossible.
4. Where agents deliver the most value
Not every business problem needs an AI agent. But when they're deployed in the right context, agents can unlock meaningful efficiency gains, new customer experiences, and even entirely new product categories. Here are the most common high-leverage entry points we've seen across industries.
🎧 Customer support and internal helpdesks
If you have a team answering repeated queries or processing similar requests all day — whether it's IT password resets, HR policy questions, or customer refunds — that's prime territory for agent automation. Agents can handle 60-80% of volume with little human oversight, escalating edge cases when needed.
📋 Admin-heavy, rules-based processes
Think booking systems, onboarding forms, compliance paperwork, and data entry. These processes often involve the same predictable steps and API calls every time. An agent can run them start to finish, calling APIs, sending emails, and filling forms with minimal fuss.
🔍 Personalised data aggregation or insights
When someone needs an answer that's buried across five systems — like "Which of our customers in France bought this last year and still haven't renewed?" — agents shine. They can retrieve data, combine it meaningfully, and present it back in a human-readable form.
📊 Self-service reporting, research, or analytics
Agents can act as research assistants that run reports, investigate competitors, or pull policy summaries from vast knowledge bases. This is ideal for sales, legal, finance, and strategy teams — especially where turnaround speed is more important than perfect formatting.
⚡ Dynamic optimisation and feedback loops
When the environment changes regularly — like delivery routes, pricing strategies, or inventory levels — agents can close the loop by sensing changes, making adjustments, and then monitoring outcomes. These are especially valuable in logistics, marketplaces, and ops-heavy businesses.
In all these cases, what matters is this: agents don't just respond — they act. That ability to make decisions, carry out multi-step processes, and only loop in a human when needed is what separates agent-based systems from traditional automation.
These are just a few examples, and the scope and reach of AI agents continue to change and grow every day. You may come up with ideas for innovations that save time or deliver customer value which do not fit into any of the sections above—and that's absolutely fine. That does not mean these ideas are not possible. The examples above simply show how people have benefited from AI agents so far and might help inspire you as you consider how to use AI agents in your own business.
5. How to get started — and what to watch out for
If you're exploring AI agents for your organisation, you don't need to overhaul everything at once. The most successful rollouts tend to start small, iterate fast, and focus on real problems your team faces today.
🎯 Start with a focused use case
The best first projects are narrow but high-impact — for example, automating basic IT requests, speeding up sales reports, or triaging support tickets. You'll learn the quirks of agent behaviour without putting sensitive processes at risk.
🛠️ Choose the right model and tools
Depending on your needs, you might build your agent using OpenAI's Assistants API, Anthropic's Claude, open-source frameworks like LangChain or CrewAI, or even your own orchestration layer. The goal isn't just picking a model — it's defining how the agent plans, remembers, and acts across tools.
🔧 Focus on toolability
Rather than giving agents access to everything, think about what APIs, databases, or scripts you can safely expose. You can always add more capability later. A read-only inventory lookup or helpdesk database is often a great first step.
🧪 Test with real-world messiness
AI agents often behave perfectly in test environments and then fail in live use when faced with typos, ambiguity, or partial data. Run pilot programs with real user input early — and watch what breaks. That's where the biggest learning happens.
👁️ Build in oversight and observability
Even with strong models, you need logging, approval workflows, and fallback paths. Most agent platforms let you store full transcripts of decisions and tool calls, which is critical for debugging and trust-building. Start with a "human-in-the-loop" approach and scale autonomy over time.
💭 Think beyond chat
While many agents use chat as their interface, don't stop there. Agents can live inside email workflows, Slack, dashboards, CRMs, or even internal dev tools. The interface is just the entry point — the magic is in what the agent can do once inside your system.
Done well, agents become not just assistants but collaborators — ones who remember, adapt, and keep getting better the more they're used. The payoff isn't just lower costs or faster service. It's a business that can respond to complexity and scale human capability with intelligence.
6. SaaS vs. Custom Development: Why Code Wins
What is SaaS? SaaS stands for "Software as a Service." It refers to cloud-based software platforms that you access over the internet, usually by paying a monthly or annual subscription fee. Instead of installing and maintaining software on your own servers, you use a ready-made solution hosted and managed by a third-party provider. Examples include Salesforce, Slack, and most modern web apps.
When building AI agents, you have two main paths: plug-and-play SaaS platforms or custom development using Python and AI libraries. SaaS agent platforms let you quickly set up and deploy agents with minimal coding, handling infrastructure, updates, and integrations for you. However, while SaaS solutions promise quick setup, coding your own agents offers significant advantages for serious deployments.
🏗️ The Custom Development Advantage
Direct API Access at Provider Rates — When you build with code, you pay OpenAI, Anthropic, or other providers directly at their wholesale rates. SaaS platforms typically mark up these costs by 2-5x to cover their infrastructure and profit margins. For high-volume deployments, this difference can mean thousands of dollars monthly.
Multi-Model Flexibility — Custom agents can seamlessly combine different models for different tasks. Use the latest complex reasoning model from OpenAI, Claude, for long-context analysis, and local models for sensitive data processing — all within the same workflow. SaaS platforms typically lock you into their preferred model or charge premium rates for model switching.
True Architectural Control — With libraries like LangChain, OpenAI Agents SDK, CrewAI, or AutoGen, you can implement any of the architectural patterns we discussed earlier. Want a Router → Specialist setup with custom memory management? Easy. Need a Perceive → Plan → Act loop with specific tool integrations? You control every component.
🔧 Technical Benefits of Coding Your Own
- Custom Tool Integration — Direct API calls to your existing systems without middleware. No waiting for SaaS providers to add integrations or paying for unnecessary features.
- Data Privacy and Security — Your data stays within your infrastructure. No third-party processing or storage concerns, crucial for regulated industries.
- Performance Optimisation — Fine-tune response times, implement caching strategies, and optimise for your specific use patterns. SaaS solutions are built for the average case, not your edge cases.
- Debugging and Observability — Full visibility into agent decision-making processes. Log every API call, reasoning step, and tool invocation for comprehensive troubleshooting.
📚 Popular Development Frameworks
OpenAI Agents SDK — OpenAI's official SDK for building, deploying, and managing agents using their Assistants API. It provides high-level abstractions for tool integration, memory, and multi-step workflows, making it easy to create production-ready agents with minimal boilerplate. Ideal for teams standardizing on OpenAI's ecosystem and looking for rapid iteration with robust support.
LangChain — The most mature ecosystem with extensive tool integrations and pre-built components. Great for rapid prototyping and production deployments. This is the go-to choice if you'd like to work with multiple AI vendors, but it requires a bit more configuration than the OpenAI Agents SDK.
CrewAI — Specialises in multi-agent coordination. Excellent for implementing the Planner + Parallel Workers pattern with role-based agent hierarchies.
AutoGen (Microsoft) — Focused on conversational multi-agent systems. Perfect for building agent teams that collaborate through natural language.
Haystack — Optimised for RAG and document processing workflows. Ideal when your agents need deep integration with knowledge bases.
Custom Python + Direct APIs — Maximum flexibility using requests, asyncio, and your preferred ML libraries. Best for unique architectures or when you need complete control.
💰 Cost Comparison Reality Check (Approx. Hypothetical Example)
Let's say you're processing 100,000 agent interactions monthly:
- SaaS Platform: $2,000-5,000/month (including markup, platform fees, and limited customisation)
- Custom Development: $400-800/month in direct API costs + development time
The break-even point is typically reached within 2-3 months of development, after which custom solutions offer 60-80% cost savings while providing superior capabilities. Useful when these costs can scale with user growth, or the number of tasks you ultimately delegate to the AI Agents.
When to Choose Code Over SaaS
Build custom agents when you need:
- Integration with proprietary systems or unusual data formats
- Specific compliance requirements (HIPAA, SOC2, etc.)
- High-volume processing where cost efficiency matters
- Multi-model workflows or experimental architectures
- Complete control over user experience and branding
SaaS solutions work for quick prototypes or very simple use cases, but they quickly become limiting as your requirements grow, especially if you need an Agent that integrates deeply with your product offering or existing systems. The most successful agent deployments we've seen started with custom development from day one.
The there are Vendor Lock-In Risks of SaaS. If you relying on a SaaS agent platform means you’re at the mercy of their business decisions. If the platform changes its pricing model, restricts features, or even shuts down, your entire workflow can be disrupted overnight. This isn’t hypothetical: remember when Google abruptly shut down its low-code App Maker platform in 2021, leaving businesses scrambling to rebuild their internal tools elsewhere? Similar stories have played out with Parse, Heroku free tier, and other once-popular platforms.
With custom code, you control your stack. If one AI provider raises prices or changes terms, you can often swap in another (e.g., switching from OpenAI to Anthropic, or to open-source models) with minimal disruption. This flexibility is crucial for long-term resilience and cost control — and it’s something SaaS platforms rarely offer.
Getting Started with Custom Development
Begin with a simple Python script that calls the OpenAI API directly. Add LangChain for tool integration, implement basic memory with a local database, and gradually build complexity. The learning curve is steeper initially, but the long-term benefits — cost savings, flexibility, and control — make it worthwhile for any serious agent deployment.
Remember: every major AI agent success story we covered earlier was built with custom code, not SaaS platforms. There's a reason for that.
7. Understanding AI Agent Limitations
While the agent revolution is real and happening now, it's important to understand the current boundaries of what's possible. Even the most sophisticated AI agents face several key limitations that shape how and where they can be deployed effectively.
⚠️ Current Technical Limitations
- Hallucination and Confidence Gaps — "Hallucination" is when AI generates plausible-sounding but incorrect information (like confidently stating a fake statistic). Despite impressive capabilities, agents can still do this, especially when working with incomplete data or unusual situations. This makes them less suitable for high-stakes decisions without human oversight.
- Context Window Constraints — Most agents have limited "working memory" (like a person who can only hold 7 things in mind at once) that restricts how much information they can process simultaneously. Complex multi-step tasks that require tracking dozens of variables can still overwhelm current systems.
- Tool Integration Fragility — Agents rely heavily on APIs and external tools, making them vulnerable to changes in those systems. A simple API update can break an entire workflow, requiring constant maintenance and monitoring.
- Reasoning Depth Limits — While agents excel at pattern recognition and routine problem-solving, they struggle with deep causal reasoning, creative breakthrough thinking, and tasks requiring genuine understanding of physical or social dynamics.
💼 Business and Deployment Challenges
- Explainability and Trust — Many organisations struggle to explain why an agent made a particular decision, especially in regulated industries. This "black box" problem limits adoption in finance, healthcare, and legal contexts.
- Training Data Bias — Agents inherit the biases present in their training data, potentially perpetuating unfair or discriminatory outcomes in hiring, lending, or customer service applications.
- Cost and Resource Management — Running sophisticated agents can be expensive, especially when they make numerous API calls or require frequent model updates. The economics don't always work for lower-value tasks.
🚀 Where We're Heading: 2025-2030 Predictions
- Specialisation Over Generalisation — Rather than building "do-everything" agents, we'll see more domain-specific agents that excel within narrow contexts. Think medical diagnostic agents, legal research agents, or financial analysis agents with deep, specialised knowledge.
- Hybrid Human-AI Workflows — The future isn't fully autonomous agents, but rather seamless collaboration between humans and AI. Agents will handle routine tasks and data processing, while humans focus on creative problem-solving, relationship building, and strategic decision-making.
- Federated Agent Networks — Instead of single super-agents, we'll see networks of specialised agents that can communicate and coordinate with each other. A customer service agent might seamlessly hand off to a billing agent, then to a technical support agent, all within one conversation.
- Continuous Learning and Adaptation — Next-generation agents will learn from every interaction, building institutional memory and adapting to your organisation's specific context over time. This personalisation will make them dramatically more effective than today's general-purpose models.
- Regulatory and Ethical Frameworks — By 2027-2028, we expect comprehensive regulations around AI agent deployment, especially in sensitive sectors. This will drive standardisation but also create competitive advantages for companies that build ethical, transparent systems early.
The Strategic Implication
Understanding these limitations isn't about dampening enthusiasm — it's about making smarter deployment decisions. The companies that will win in the agent era are those that start now, learn from early mistakes, and build systems that complement rather than replace human judgment.
The key insight? Agents don't need to be perfect to be transformative. They just need to be better than the status quo at specific, well-defined tasks. And on that measure, they're already exceeding expectations.
8. Final thoughts: agents are not the future — they're now
What was once science fiction — autonomous software agents that reason, plan, and act — is now quietly being embedded into the workflows of forward-thinking companies. The tools exist. The results are measurable. And the barriers to entry have never been lower.
Whether you're trying to free up staff time, create better customer experiences, or unlock new lines of business, AI agents offer a way to scale your best people without simply hiring more. They don't sleep. They don't forget. And they can get smarter every time you use them.
But the real opportunity isn't just about automation. It's about augmentation — amplifying the capabilities of your team, making complex systems more navigable, and opening up new ways of thinking about problem-solving entirely.
If you're serious about exploring what AI agents could mean for your organisation, don't wait. Start small. Move fast. And think systemically. Because once you've seen what's possible, you won't want to go back.
The agent era is here. Let's build it — on your terms.
Ready to build your own AI agent?
If you're considering custom AI agent development or want expert advice on integrating agents into your business, Scorchsoft can help.
Contact Scorchsoft for a free consultation
Examples of AI Projects
Here are examples of AI projects we've developed:
AI Agent Engine
A framework for conceptualising and deploying AI agents. Supports the "Router → Specialist Sub-agents" workflow and others outlined earlier in this article.
QuickWhisper - Speech to CopyEdited Text App
A desktop app that runs in the background and uses AI to transcribe your voice, copy edit, and insert into your active application. This is a useful productivity tool we developed for internal use, but it became so popular that we decided to open-source it. This tool is free to download and use.
SalesChamp Live AI Sales Coaching App
Sales Champ is an agentic application that follows the perceive-plan-act loop. It runs in the background to monitors live sales calls, listens to both audio streams, transcribes them live, and provides agentic feedback in real time. It also sends the generated transcript to a back-end report generation tool, giving sales managers insights into salesperson performance.
Text-to-Mic - An Accessibility Tool that Gives the Mute Community Their Voice Back
TextMic is an accessibility tool that we have also open-sourced to support the mute community. This AI tool follows the single-agent plus skill architectural pattern. It can convert speech to text and text to speech, perform copy editing, and manipulate text to deliver speech in a chosen tone preset. It can also connect to a microphone feed, allowing users who cannot speak to participate in meetings. This tool is free to download and use.
AI Phone Agent Manager & Enablement Package
The AI phone agent manager provides a comprehensive interface that allows users to manage AI phone agents—autonomous AI agents configured to answer calls at a specific number, hold real conversations, and trigger tools based on customer phone interactions. These tools might include making bookings or entering information into your ticketing or customer relationship management systems.
AI phone agents demonstrate a perceive-plan-act loop. Depending on how you set up the tools, you can also configure them as routers with specialist sub-agents or as a single agent with additional skills.
Other examples of AI projects we've developed include:
- AI Retrieval-Augmented Generation (RAG) agents that classify information using a vector embedding stored within a vector database. They allow users to retrieve documents or tickets based on the proximity of documents within the vector space.
- Automated search and retrieval agents that generate insights or documents by following the planner-and-parallel-worker methodology.
- AI-enabled classification systems that autonomously review tickets created in project management systems and classify them by bug category or other relevant groupings. This improves assignment accuracy and helps generate better insights.
- Schedule AI-enabled tasks to generate summary reports from large collections of text data, such as customer reviews.
- AI converters for non-structured to structured data can process inputs such as training programmes provided in various formats. These converters normalise and store the data in a standardised way, enabling effective data insights.
- Systems have integrated AI chatbots with tools and MCP server connections, providing an alternative user interface. This allows users to connect to and interact with the system more easily.
- Automatic image tagging agents, which caption assets and auto-generate curated social media content.
If you have an idea for an AI agent project and would like our support, please contact us or book a free strategy call. We will let you know if we can support your project.
Sources
For further reading and verification of the claims made in this article, see: Survey on Evaluation of LLM-based Agents (arXiv, 2025).
Glossary of Terms
- AI Agent: An autonomous software program that can reason, plan, and act to accomplish tasks, often using large language models (LLMs) and external tools.
- SaaS (Software as a Service): A software delivery model where applications are hosted by a third-party provider and accessed over the internet, typically via subscription.
- LLM (Large Language Model): An artificial intelligence model trained on vast amounts of text data to understand and generate human-like language.
- Hallucination: When an AI system generates information that sounds plausible but is actually incorrect or fabricated.
- Context Window: The amount of information (such as words or tokens) an AI model can consider at one time when generating responses.
- Tool Integration: The process of connecting AI agents to external software, APIs, or databases to extend their capabilities.
- Vendor Lock-In: A situation where a customer becomes dependent on a single provider for products or services, making it difficult to switch to another provider without substantial costs or inconvenience.
- Federated Agent Networks: Systems where multiple specialized AI agents communicate and collaborate to solve complex tasks.
- Hybrid Human-AI Workflow: A collaborative process where humans and AI agents work together, each handling tasks suited to their strengths.
- Bias: Systematic errors in AI outputs caused by prejudices or imbalances in the training data.
- Prompt Engineering: The practice of designing and refining input prompts to guide AI models toward producing more accurate, relevant, or useful outputs.
- API (Application Programming Interface): A set of rules and protocols that allows different software applications to communicate and interact with each other.
- Fine-tuning: The process of taking a pre-trained AI model and training it further on a specific dataset to specialize its behavior for particular tasks or domains.
- Indexing: The process of organizing and cataloging data to make it quickly searchable.
- Inference: The process of using a trained AI model to generate predictions or outputs based on new input data.
- JSON (JavaScript Object Notation): A lightweight data format commonly used for storing and exchanging structured data.
- LangChain: A popular framework for building applications with large language models, offering extensive tool integrations and pre-built components.
- Latency: The delay between making a request and receiving a response from an AI system.
- Markdown: A lightweight markup language for formatting text documents.
- Middleware: Software that acts as a bridge between different applications or systems.
- Observability: The ability to monitor and understand the internal state of a system through its outputs.
- Orchestration Layer: Software that coordinates and manages the execution of multiple AI agents or tools.
- Schema: A structured framework that defines how data should be organized and formatted.
- SDK (Software Development Kit): A collection of tools, libraries, and documentation for building applications on a specific platform.
- Semantic Search: A search method that finds information based on meaning and context rather than exact keyword matching.
- Throttling: Limiting the rate at which actions can be performed to prevent system overload.
- Throughput: The amount of work or data processed by a system in a given time period.
- Vector Database/Vector Search: Specialized databases that store embeddings and enable semantic search based on meaning rather than exact text matching.
- Webhook: A method for one application to automatically send data to another when specific events occur.
- Zero-shot Learning: The ability of an AI model to perform a task or answer a question it has never explicitly seen during training, by leveraging general knowledge.
- Chain-of-Thought Reasoning: A technique where an AI model generates intermediate reasoning steps to arrive at a final answer, improving transparency and accuracy.
- Retrieval-Augmented Generation (RAG): An approach where an AI model retrieves relevant information from external sources (like databases or documents) to inform its generated responses.
- Autonomous Workflow: A sequence of tasks or processes that an AI agent can execute end-to-end with minimal or no human intervention.
- AutoGen: Microsoft's framework for creating conversational multi-agent systems that collaborate through natural language.
- Batch Processing: Processing data in groups rather than individually in real-time, often used to balance performance with cost efficiency.
- Boilerplate: Standard, repetitive code that serves as a template for common functionality, reducing development time.
- Chunking: The process of breaking large documents into smaller, manageable pieces for processing by AI models.
- CrewAI: A framework specialized for building multi-agent AI systems with role-based agent hierarchies.
- Embeddings: Mathematical representations of text, images, or other data that capture semantic meaning in a format AI models can process.
- Guardrails: Safety mechanisms or constraints put in place to prevent AI agents from producing harmful, unsafe, or undesired outputs.
- Token: A unit of text (such as a word or part of a word) that AI models use to process and generate language.
- Grounding: The process of ensuring that an AI agent’s outputs are based on factual, verifiable information, often by linking responses to trusted sources.
