AI Agents & Automation
We build intelligent agents that handle real work — reasoning, acting, and learning alongside your team.
What Are AI Agents?
AI agents go beyond chatbots. They can reason, use tools, access data, take actions, and work autonomously on complex tasks. They represent a significant evolution in business automation.
Beyond Chat
Chatbots answer questions. Agents get things done. They break down complex goals into steps, decide which tools to use, and execute — with human oversight where it matters.
Reasoning & Acting
Modern agents combine large language models with tool use, memory, and planning. They can search databases, call APIs, write code, draft documents, and learn from feedback.
Agent Platforms We Build With
We're vendor-agnostic. We choose the right platform for your problem, not the other way around.
LangChain / LangGraph
Flexible agent orchestration with tool use, memory, retrieval, and chains of thought. The go-to framework for sophisticated agent workflows.
CrewAI
Multi-agent collaboration where role-based AI teams work together. Ideal for complex tasks that benefit from specialised agents cooperating.
AutoGen (Microsoft)
Conversational multi-agent frameworks for building groups of agents that discuss, debate, and collaborate to solve problems.
OpenAI Assistants API
Function calling, code interpreter, and retrieval capabilities. Production-ready agents backed by GPT models with built-in tool execution.
Anthropic Claude Agent SDK
Tool use, computer use, and agentic workflows powered by Claude. Excellent for tasks requiring careful reasoning and nuanced judgement.
Amazon Bedrock Agents
Enterprise-grade agents with native AWS integration. Ideal for organisations already invested in the AWS ecosystem with strict compliance needs.
Google Vertex AI Agents
Grounded agents with Google Search and enterprise data integration. Strong for tasks requiring real-time information and Google Workspace connectivity.
n8n / Make / Zapier
No-code and low-code agent workflows for rapid automation. Perfect for quick wins and connecting AI to hundreds of business apps without custom development.
Custom Agent Frameworks
Bespoke architectures when off-the-shelf isn't enough. We design and build custom agent systems tailored to your unique requirements.
What Agents Can Do for Your Business
AI agents work across every function. Here are the use cases delivering real value today.
Customer Service Agents
Handle enquiries, resolve common issues, and escalate intelligently to human staff when needed. Available 24/7 with consistent quality.
Document Processing Agents
Extract, classify, and summarise documents at scale. Process invoices, contracts, reports, and forms without manual effort.
Research Agents
Gather, analyse, and report on market intelligence, competitive landscape, and industry trends. Turn hours of research into minutes.
Sales Agents
Qualify leads, draft proposals, manage follow-ups, and keep your pipeline moving. Consistent outreach without the manual grind.
Internal Knowledge Agents
Answer staff questions from company knowledge bases using RAG. Surface the right information from policies, procedures, and documentation instantly.
Code Review & Development Agents
Assist engineering teams with code review, bug detection, documentation generation, and development workflow automation.
Data Analysis Agents
Query databases, generate reports, and surface insights. Turn natural language questions into SQL queries and actionable dashboards.
Compliance Agents
Monitor regulatory changes, flag risks, and ensure your organisation stays ahead of compliance obligations automatically.
Agent Topologies
There is no one-size-fits-all architecture for AI agents. The right topology depends on task complexity, latency requirements, and how much autonomy you need. Here are the patterns we design and deploy.
Single Agent
One LLM with a set of tools, working autonomously on a focused task. The simplest topology and often the right starting point. Well suited to customer support, document processing, data extraction, and internal Q&A.
Sequential / Pipeline
Agents pass work to each other in a defined order, each specialising in one step. Think research agent → analysis agent → report-writing agent. Predictable, debuggable, and easy to monitor.
Hierarchical / Manager-Worker
A manager agent breaks down complex goals, delegates sub-tasks to specialist worker agents, and aggregates results. Ideal for multi-step workflows that require coordination across different domains or skill sets.
Collaborative / Swarm
Multiple peer agents discuss, debate, and reach consensus without a central controller. Powerful for brainstorming, code review, adversarial testing, and any task that benefits from diverse perspectives.
Router / Dispatcher
A lightweight router agent analyses the incoming request and routes it to the best specialist agent. Low latency, efficient resource use, and easy to scale by adding new specialist agents behind the router.
Supervisor with Human-in-the-Loop
The agent works autonomously on routine decisions but escalates to a human for high-stakes, ambiguous, or policy-sensitive situations. Balances efficiency with accountability — critical for regulated industries.
Reflection / Self-Critique
The agent reviews its own output, identifies weaknesses, and iterates before delivering a final result. Dramatically improves quality for writing, coding, analysis, and any task where a second pass catches errors.
Cutting-Edge Agent Concepts
The agent landscape is evolving rapidly. These are the patterns and capabilities shaping the next generation of AI systems — and we work with all of them.
Tool Use & Function Calling
Agents that go beyond text generation to call APIs, query databases, execute code, browse the web, send emails, and interact with any system that exposes an interface. The foundation of useful agents.
Memory Systems
Short-term memory (conversation context), long-term memory (vector stores and knowledge bases), and episodic memory (learning from past interactions). Memory transforms stateless LLMs into agents that remember and improve.
Guardrails & Safety
Input and output validation, content filtering, cost limits, rate limiting, and toxicity detection. Production agents need multiple layers of protection to operate safely and reliably in business environments.
Agent Evaluation & Testing
Benchmarking agent performance across accuracy, latency, cost, and safety. Automated testing frameworks, regression suites, and evaluation datasets ensure agents work correctly before and after deployment.
Agentic RAG
Agents that dynamically decide what to retrieve, from which source, and how to use it — rather than following a fixed retrieval pipeline. They can reformulate queries, cross-reference multiple knowledge bases, and verify retrieved information before responding.
Computer Use / Browser Agents
Agents that navigate GUIs, fill out forms, click buttons, and interact with web applications just like a human would. Unlocks automation for legacy systems and applications without APIs.
Code Generation Agents
Agents that write, test, debug, and refactor code autonomously. Tools like Claude Code, GitHub Copilot, Cursor, Windsurf, and Devin are transforming software development by handling implementation while developers focus on architecture and design.
Multi-Modal Agents
Agents that process and reason across text, images, audio, and video. Analyse photographs, transcribe meetings, interpret charts, generate images, and work with any media type your business produces.
Agent Observability
Tracing agent decisions, debugging reasoning chains, monitoring token usage and costs, and identifying failure modes. Tools like LangSmith, Langfuse, Helicone, and Arize Phoenix give full visibility into what your agents are doing and why.
MCP (Model Context Protocol)
Anthropic's open standard for connecting AI agents to external tools and data sources. MCP provides a universal protocol so agents can interact with any service — databases, APIs, file systems, development tools — through a single, standardised interface.
Our Agent Stack
We are vendor-agnostic and work across the full landscape of agent technologies. Here is what we bring to the table.
RAG: Retrieval-Augmented Generation
Give your AI agents access to your organisation's knowledge — accurately and securely.
What Is RAG & Why It Matters
RAG connects large language models to your actual data. Instead of relying solely on training data, agents retrieve relevant documents at query time — reducing hallucinations and ensuring answers are grounded in your organisation's knowledge.
Vector Databases
We work with leading vector stores including Pinecone, Weaviate, Chroma, Qdrant, and pgvector. We select the right database based on your scale, latency requirements, and infrastructure preferences.
Embedding Models
High-quality embeddings are the foundation of good retrieval. We use models from OpenAI, Cohere, and Voyage AI — benchmarked and selected for your specific content type and domain.
Document Ingestion
We build pipelines that ingest PDFs, Word documents, web pages, Confluence, SharePoint, Notion, and more. Chunking strategies, metadata extraction, and refresh schedules are all handled.
Our Agent Development Process
A proven, iterative approach to building agents that deliver real business value.
Define Scope
We work with you to define the agent's scope, responsibilities, and success criteria. Clear goals from day one.
Select LLM & Tools
We choose the optimal language model, tools, and integrations based on your requirements, budget, and data constraints.
Build & Iterate
Rapid prototyping with frequent demos. We test rigorously against real scenarios and refine until the agent performs reliably.
Deploy with Guardrails
Production deployment with monitoring, logging, cost controls, and safety guardrails. Nothing goes live without oversight.
Optimise
Continuous improvement based on real usage data. We tune prompts, refine retrieval, and expand capabilities over time.
Ready to Build Your AI Agents?
Book a free 30-minute discovery call. We'll assess your automation opportunities and outline how agents can work for your business.