Agentic AI
The fundamental idea behind agentic AI is simple: it's AI that <em>does things</em>. Not just generates text — it takes actions, moves data, and interacts with real systems. How much autonomy it has, and how it's deployed, depends entirely on your use case.
AI that does things
Traditional AI generates outputs — text, images, summaries. Agentic AI goes further: it acts on those outputs. It sends emails, updates databases, triggers workflows, processes payments, files reports. The "doing" is what makes it agentic.
Why integrations are the core
If agentic AI is defined by doing things, then integrations are how it does them. Every action an agent takes — reading from a CRM, posting to Slack, updating a spreadsheet — requires a connection to an external system. This is why MCP servers and API integrations are central to every agentic project I build.
MCP: structured tool access
The Model Context Protocol gives AI agents secure, standardized access to tools and data sources. Instead of fragile custom code for each integration, MCP provides a consistent interface. I build custom MCP servers that connect agents to your internal systems — databases, APIs, file stores, and business tools.
API integrations
Not everything needs MCP. Direct API integrations remain the right choice for many connections — payment processors, notification services, third-party platforms. The key is choosing the right integration pattern for each connection point, balancing flexibility with reliability.
What it doesn't have to be
The hype around agentic AI creates rigid expectations. In practice, the most effective systems are built around what you actually need — not what the buzzwords suggest.
Full autonomy sounds exciting but is rarely the goal. Many of the most valuable agentic systems include human-in-the-loop checkpoints — a person reviews, approves, or redirects before the agent proceeds. This gives you the speed and scale of AI with the judgment and accountability of human oversight.
Not every agent needs to be a persistent, always-on background worker. Chatbots and conversational interfaces are agentic too — they take actions, query systems, and execute tasks, but only when a user is actively interacting. Server-side and on-demand deployments are equally valid.
The best solutions often combine approaches. A background agent monitors and processes data around the clock while a chatbot gives your team a conversational interface to query and direct it. Autonomous pipelines handle volume; human checkpoints handle exceptions. You can use both.
Types of AI automation
Agents, pipelines, and workflows solve different problems. Understanding the distinction helps scope the right solution for your use case.
AI Agents
Autonomous systems that observe, decide, and act. An agent monitors conditions, interprets context, and takes action without being told exactly what to do each time. They're ideal when the problem requires judgment, not just execution.
- Monitor data sources and act on triggers
- Make decisions based on context and rules
- Chain multiple actions together autonomously
Pipelines
Structured data flows that process information step by step. A pipeline takes input, transforms it through defined stages, and produces output. They're predictable, testable, and perfect for data processing at scale.
- Extract, transform, and load data reliably
- Process information through defined stages
- Scale to handle large volumes consistently
Workflows
Orchestrated sequences that coordinate tools and services. A workflow defines the order of operations — when to trigger, what to do, and where to send results. They connect your existing systems into coherent automated processes.
- Connect multiple tools and services
- Define trigger conditions and routing logic
- Coordinate complex multi-step operations
What I build
Based on what makes sense for your use case, here's what I build. Workflows, data pipelines and dashboards without an agent in them sit under Automation; the models inside an agent can be trained for the job.
Human-in-the-Loop Agents
Agents that handle the heavy lifting — data processing, classification, drafting, routing — but pause for human review at critical decision points. You get AI speed with human accountability.
- Approval workflows before actions are taken
- Review queues for agent-generated outputs
- Escalation paths for edge cases
Chatbots & Conversational UIs
Conversational interfaces backed by real agentic capabilities. These aren't simple FAQ bots — they query live data, trigger actions, and interact with your systems through natural conversation.
- Internal tools with chat-based access
- Customer-facing assistants with live data
- Agent interfaces for querying and directing AI systems
Autonomous Back-End Agents
Agents that run independently — monitoring data sources, processing incoming information, triggering workflows, and updating systems. These are the always-on workers for tasks that benefit from continuous operation.
- Data monitoring and enrichment
- Automated classification and routing
- Event-driven processing pipelines
Combined Systems
End-to-end solutions that blend autonomous processing with human interfaces. A pipeline processes data in the background; a dashboard surfaces insights; a chatbot lets your team interact with it all.
- Back-end agents + front-end interfaces
- Automated pipelines + human review stages
- Monitoring + alerting + conversational access
RAG Pipelines & Knowledge Bases
Retrieval-Augmented Generation gives your agents access to your business knowledge in real time. I build ingestion pipelines, vector stores, and retrieval layers so agents pull from up-to-date internal data — not just their training data.
- Document ingestion and chunking pipelines
- Vector database setup (Pinecone, Weaviate, pgvector)
- Context-aware retrieval for agent workflows
MCP Server Development
Custom Model Context Protocol servers that give your agents structured access to internal systems. The integration layer that makes "AI that does things" possible.
- Database and API connectors
- Business tool integrations
- Secure access with proper authentication
Digital Wallets & Payments
When agents need to transact — processing payments, managing subscriptions, handling billing — I integrate payment systems and digital wallet capabilities into agentic workflows.
- Payment processing integrations
- Agent-managed transactions
- Financial workflow automation
How do you feel about AI automation?
(When your boss isn't looking)
Agents, pipelines, MCP — we've all heard about the potential, but are you ready to implement this?
Turning the Jigsaw into a Symphony
A chatbot here, a pipeline there, a RAG system somewhere else — AI components deliver the most value when they're connected. I specialise in bringing the full picture together, focusing on the connection points where real value lives.
Your AI System
Every piece, connected
Models
Choosing the right reasoning engine
Inference
Where and how models run
Context
RAG, memory, and knowledge
Agents
Orchestration, pipelines, workflows
MCP
Connecting models to the world
Safety
Guardrails and security harnesses
Observability
Eval, logging, monitoring
Storage
Conversations, outputs, data lakes
These pieces don't work in isolation. The real value is in how they connect.
See How It All Fits Together →Working with me on an agentic system
Agents, MCP integrations, retrieval and chat front ends, built around your systems, and where a step needs it, around a model I've trained for the job.
Who it's for
- Teams with a repetitive task that needs judgement, not just rules
- Organisations that want AI to act on their own systems, not just answer questions
- Teams with an agent prototype that needs to become something they can run
- Businesses that need control over where data goes and what the agent may do
What you get
- A working agent system in production
- MCP servers or integrations to your tools and data
- Guardrails, approvals and logging set to the autonomy you choose
- Deployment on the infrastructure you pick
- Handover documentation; you own all the code
What I need from you
- A description of the task and who does it today
- Access (or sandbox access) to the systems the agent will use
- Your constraints on data, hosting and approvals
- A contact who owns the process
How it runs
Typically 10–14 weeks for a new system; focused pieces are shorter.
- Weeks 1–2
Discovery
Map the task, systems, data and how much autonomy is acceptable.
- Weeks 2–3
Architecture
Agents, integrations, models and deployment, agreed before build.
- Weeks 3–10
Build
Integrations first, then agent logic, tested against real cases.
- Weeks 10–14
Deploy and hand over
Production rollout, monitoring and documentation.
For a focused pilot. Full agent systems are scoped after a review and paid against milestones.