Service
AI Agent Development
We build AI agents that operate inside your business systems — reading records, calling APIs, writing back results and escalating to a human when the decision warrants it.
What is AI agent development?
AI agent development is the engineering of software that uses a language model as a reasoning layer, gives it tools — API calls, database queries, document readers — and runs it inside a controlled loop that can take real actions in business systems. Unlike a chatbot, which returns text, an agent completes a task: it retrieves the record, applies the rule, updates the system and reports what it did.
In practice most of the work is not the model. It is the tool layer, the permissions around it, the retrieval of trustworthy context, the validation of every write, and the audit trail that lets a human review what the agent did and why. That engineering is what separates a demo from a system a business can rely on.
Most organisations that experiment with AI end up with something that answers questions. That is useful, but it does not remove work. The value appears when the model can act: pull the contractor record, check it against the requirement matrix, generate the document request, send it, and log the outcome against the job.
We design agents as ordinary production software with a probabilistic component inside them. The model decides; the surrounding system constrains, validates and records. Tool calls are typed and permissioned. Writes are idempotent. Anything consequential routes through an approval step with a human name attached to it.
Business problems
What this usually solves.
The situations clients describe when they start this conversation.
Staff spend hours moving data between systems
Information is copied from email into a CRM, from a PDF into a spreadsheet, from a portal into an ERP. The work is rule-based but too unstructured for traditional automation.
Pilots never reach production
A prototype works in a notebook and then stalls because nobody has solved authentication, permissions, error handling, cost control or auditability.
No confidence in what the model did
Without structured logging of prompts, tool calls and outcomes, teams cannot verify behaviour, debug failures or satisfy internal review.
Unbounded cost and latency
Naive implementations re-send entire documents on every turn and call the largest model for every step, making the economics unworkable at volume.
Capabilities
What we bring to it.
Tool and function design
Typed tool definitions over your REST, GraphQL or SQL surfaces, with argument validation, scoped credentials and idempotency keys on every write.
Retrieval and grounding
Document ingestion, chunking and hybrid search so the agent reasons over your actual policies, contracts and records rather than general knowledge.
Orchestration and control flow
Deterministic state machines around the model: bounded retries, step limits, fallbacks and explicit termination conditions.
Human-in-the-loop approval
Review queues where a person sees the proposed action, the evidence behind it and the systems it will touch before it executes.
Evaluation harnesses
Regression suites of real cases with expected outcomes, run on every change so prompt and model updates are measured rather than guessed.
Observability and cost control
Structured traces of every run — inputs, tool calls, tokens, latency, outcome — with caching and model routing to keep unit economics predictable.
Deliverables
What we build
- Operational agents embedded in internal tools and admin portals
- Document processing agents that extract, validate and file structured data
- Support agents with ticket context, knowledge retrieval and safe write-back
- Research and enrichment agents that assemble briefs from multiple sources
- Scheduling and coordination agents that negotiate across calendars and systems
- Compliance agents that check records against requirement rules and flag gaps
Stack
Technology approach
We stay model-agnostic and keep the provider behind an internal interface, so a model change is a configuration decision rather than a rewrite.
| Layer | What we use |
|---|---|
| Reasoning | Frontier and small models selected per step by cost and accuracy |
| Tools | Typed function calls over REST, GraphQL, SQL and internal services |
| Retrieval | PostgreSQL with pgvector, hybrid keyword plus semantic search |
| Runtime | Node.js services on Azure or AWS, queue-backed for long-running work |
| Interface | Next.js consoles for review, approval and trace inspection |
| Telemetry | Per-run traces, token accounting and evaluation dashboards |
Process
How we deliver.
- 01
Discover
Understand business requirements and existing systems.
- 02
Architect
Design product, cloud, integration and data architecture.
- 03
Engineer
Build production-grade software.
- 04
Launch
Deploy, integrate and validate.
- 05
Scale
Optimize, monitor and evolve.
Case studies
Relevant work
FAQ
AI Agent Development — questions we are asked
How is an AI agent different from a chatbot?
A chatbot produces text. An agent produces an outcome. It has tools it can call, permission to use them, and a loop that continues until the task is complete or it escalates. The engineering difference is the tool layer, validation and audit trail around the model.
Can an agent write to our production systems?
Yes, and it should be constrained when it does. We scope credentials to the narrowest possible permission, make writes idempotent, validate every payload against a schema before it is sent, and put an approval step in front of anything financial, contractual or customer-facing.
Which model do you use?
Whichever fits the step. Classification and extraction often run well on small, cheap models; multi-step reasoning needs a frontier model. We keep the provider behind an interface and route per step, so cost and accuracy can be tuned without changing application code.
How do you stop the agent doing something wrong?
Four layers: restricting what tools exist at all, validating arguments before execution, bounding the loop with step and retry limits, and requiring human approval for consequential actions. Everything is traced, so incorrect behaviour is diagnosable rather than mysterious.
How long does a first agent take to build?
It depends entirely on how accessible your systems are. Where clean APIs exist, a narrowly scoped agent with a review queue is a matter of weeks. Where integration has to be built first, the integration work dominates the timeline.
Planning ai agent development work?
Tell us about the systems involved and the constraints. We will come back with an architecture and a delivery sequence.