Plenty of people are stringing together off-the-shelf AI products to create an AI agent, workflow or tool. That is not exactly AI engineering. It is more like producing a working proof of concept, and as such it will often do strange things the moment it hits messy data, a weird edge case or some other anomaly (such as a human being).
Real AI engineering is the unglamorous work behind robust AI systems: architecture, orchestration, guardrails, testing, and, importantly, deciding what the AI should never be trusted to do on its own. It is the difference between "look what it can do, mostly" and "this runs part of our business now."
At Putti, we work across three areas of AI engineering.
AI Application Engineering
This is about building AI into your software, not just connecting to it. Whether you are adding functionality to an existing custom application or including it in a new one, there is now a whole realm of powerful capabilities that can be built seamlessly into your software. This includes:
- Natural language processing (NLP)
- Computer vision and image recognition
- AI-powered search or chat
- Intelligent document analysis, processing or production
- Workflow automation
- Text and image generation
- AI-powered insights
- Personalisation via AI that learns about the user
- Sentiment analysis
- And much more
Integrating these usually goes well beyond calling an API or dropping in an SDK. Depending on the application, there are usually processes that need setting up around data preparation, model implementation, orchestration and more.
The whole system also needs to be engineered with sensors and verifications that ensure a consistent quality of work, or provide an appropriate response when it meets something completely outside its remit.
AI Agent Engineering
AI agents are kind of the new apps, but like apps got promoted to coworkers. An AI agent might be fully autonomous, or still include points of human interaction. Some follow a simple linear workflow, while others have complex decision logic, multiple software (API) integrations, and perform various specialised tasks.
You can generally distil an AI agent down to:
- Data source(s)
- AI(s), usually LLMs
- Built-in prompts and prompt construction
- Decision points and actions
- Potentially calls to external tools
These are all strung together with some code. More complex agents also include orchestration and validation, or are even structured to use other agents.
Everyone is talking about agents because they are powerful enablers of business automation: the twenty-minute admin task that now happens invisibly after a customer or staff action, while that person gets on with something more important.
A key part of the craft of AI agent engineering is restraint: giving an agent enough rope to be genuinely useful, without enough to hang your business on a bad decision at 2am. Every agent needs boundaries, logging, validation and an off-switch.
In general, AI agents are powerful and productive, and so they need building with robust principles applied from the ground up.
AI Brain Engineering
Underneath every sophisticated AI application or agent sits a "brain": the harness, memory, context and reasoning layers that let the AI understand your business and work effectively within it, rather than just confidently generalising from knowledge it gained on the internet.
It is the least visible layer and, frankly, the one most often skipped in lighter-weight projects. Get it wrong and you get an AI that sounds authoritative and is quietly, fluently wrong. Getting it right means structuring your organisational knowledge so the system can reason over it accurately and stay current as your business changes. It also means providing orchestration that correctly controls the AI's behaviour, gathers context, invokes tools, manages memory and processes outputs.
At Putti we scope for exactly what you need and build AI brains designed for your context.