AI Agents
Agents That Do the Work, Not Just Describe It.
An agent earns its place when it can reason about a request, reach into the systems that hold the answer, take an action with consequences, and know when to stop and ask a person. Everything short of that is a chat window.
The Problem
Most agent projects stall at the demo.
An agent earns its place when it can reason about a request, reach into the systems that hold the answer, take an action with consequences, and know when to stop and ask a person. Everything short of that is a chat window.
A model that answers questions about your documentation is straightforward. An agent that files the claim, updates the record, reconciles the exception and leaves an audit trail is a distributed system with a language model inside it — and it fails in the ways distributed systems fail.
The hard parts are not prompts. They are tool contracts a model can use without ambiguity, retrieval that returns the right passage rather than a similar one, actions that are idempotent under retry, and an escalation path a human actually receives.
We build for the second case. That means deciding early what the agent may do unilaterally, what it must propose, and how every step is recorded — before the first prompt is written, because those decisions determine the architecture rather than the wording.
Our Approach
How we work on this.
The same order every time. Understanding before design, design before build, evidence before scale.
- 01
Scope the decision, not the chat
We start from a task with a defined output and a defined owner: which requests, which systems, which outcomes count as done. An agent without a boundary cannot be evaluated, and anything that cannot be evaluated cannot be trusted with an action.
- 02
Model the tools first
Each capability becomes an explicit, typed tool with validation, permissions and idempotency. The model chooses between tools; it never improvises against a raw API. Most reliability work happens here, not in the prompt.
- 03
Ground the reasoning
Retrieval over your own corpus, with chunking and ranking tuned to how the documents are actually written, and citations attached so an answer can be checked rather than believed.
- 04
Design the handoff
Confidence thresholds, escalation routes and the state a person inherits. A good agent hands over a prepared case with the evidence assembled — not a transcript for someone to read.
- 05
Evaluate continuously
A fixed set of real cases, scored on task completion rather than fluency, run on every prompt, model or tool change. Regressions in agent systems are silent otherwise, and they are found by customers.
Capabilities
What this covers.
Named plainly, so you can tell whether we are the right people for the piece of work you have.
- AI Agents
- Multi-Agent Systems
- RAG
- LLM Integrations
- Workflow Automation
- Enterprise AI Assistants
- Tool-Using Agents
Technical Architecture
How the system is put together.
Use Cases
Where this applies.
If one of these reads like a description of your week, it is the right conversation to start with.
Tier-one support resolution
The agent reads the ticket, retrieves account state and the governing policy version, performs the eligible action — refund, reissue, reschedule — and escalates anything outside policy with the diagnosis already written.
Back-office exception handling
Invoice mismatches, failed reconciliations, stuck orders. The agent gathers evidence across systems and proposes a correction; a person approves it and the approval is part of the record.
Internal knowledge with authority
An assistant over runbooks, contracts and specifications that cites the clause it used and the version it read, so an answer can be verified instead of trusted.
Multi-agent operations
A planner decomposing work across specialised agents — retrieval, calculation, system-of-record writes — under a supervisor that enforces budget, scope and stopping conditions.
CRM and pipeline hygiene
Enrichment, deduplication, meeting summarisation and follow-up drafting, written back under a service identity with a full audit trail rather than through someone’s personal credentials.
Engineering workflows
Triaging incoming issues, reproducing bugs against a test harness, and opening a change with the diff and the reasoning attached for a human to review.
Business Impact
What changes.
Stated as outcomes rather than percentages. We publish numbers for work we measured, on the page for that work.
Work that queued behind a person’s availability now runs continuously and predictably.
Every agent action is attributable: which tool, which inputs, which policy version, which outcome.
Escalations arrive with the context assembled, so human time goes to judgement rather than retrieval.
Model and vendor changes become swappable, because the reasoning layer sits behind an interface you own.
Quality is measurable, because the system is scored against real cases rather than reviewed by impression.
Scope expands safely: new capability means a new tool with its own permissions, not a longer prompt.
Related Work
Worked examples.
Realistic problems taken through architecture, implementation and the trade-offs we would argue for in a real engagement.
- DemonstrationIndustrial manufacturing
Intelligent Operations Platform
A reference architecture for unifying plant telemetry, work orders and quality records behind one operational data model, with model-assisted triage layered on top.
- DemonstrationRegulated services
Enterprise Knowledge Intelligence
A retrieval and agent architecture for internal knowledge where permissions, provenance and refusal behaviour are requirements rather than refinements.
FAQ
Questions we are usually asked.
AI agents
Bring us a workflow, not a chatbot brief.
Tell us which decisions take too long and which systems hold the answer. We will show you where an agent belongs, where it does not, and what has to be true before it touches anything.