Task agents
Single-purpose agents that own one multi-step job end to end - dispute resolution, supplier research, incident triage, document preparation.
Agentic AI development
AI agents that finish the whole task.
Agents are the right tool when the work needs judgement across many steps and systems. We build them with the evaluation, guardrails and oversight that keep autonomy safe.
An AI agent is a system that plans, calls tools, observes results and keeps going until a goal is met. That is genuinely useful when a task spans many systems and cannot be reduced to a fixed sequence: researching a supplier, resolving a billing dispute, triaging an incident, preparing a report from six sources. It is the wrong tool for anything a deterministic automation can already do reliably, and we will tell you when that is the case.
The gap between an agent demo and an agent in production is oversight. Ours ship with scoped tool permissions, spend and step limits, full trace logging of every decision, an evaluation harness that scores task completion on real cases, and defined checkpoints where a human approves before anything irreversible happens.
The agent systems we build
Multi-agent systems
Specialised agents coordinated by an orchestrator, each with narrow tools and clear handoffs, which is far more debuggable than one agent with fifty tools.
Operations copilots
Agents embedded in your internal tools that draft the action, show the reasoning and wait for a person to approve before executing.
Research and analysis agents
Agents that gather from internal and external sources, cross-check, cite and produce a structured brief instead of a wall of text.
Engineering agents
Agents wired into your build, test and deployment systems for migration work, test generation and routine maintenance under review.
Guardrails and evaluation
The layer most teams skip: permission scoping, step and cost limits, trace logging, offline evaluation and regression suites before every release.
How we take agents to production
- 01
Decide if it needs an agent
We test whether a deterministic workflow or a single model call solves it first. Agents cost more to run and more to debug, so they need to earn the choice.
- 02
Tools and boundaries
Each tool is defined, scoped and permissioned. We set step, time and spend ceilings, and we mark which actions can never happen without human approval.
- 03
Evaluate on real tasks
A benchmark of real historical cases with known correct outcomes. We report task completion rate, intervention rate and cost per task before rollout.
- 04
Observe and tighten
Every run is traced. We review failures weekly, tighten prompts, tools and guardrails, and expand autonomy only where the numbers support it.
Where agents earn their keep
Back-office resolution
Disputes, exceptions and mismatches investigated across systems and resolved or escalated with a written rationale.
Procurement and supply
Supplier discovery, document checks and quote comparison compiled into a decision-ready brief.
Incident response
Alerts triaged, logs and metrics correlated, likely cause proposed and the runbook drafted for on-call approval.
Sales operations
Account research, CRM hygiene and personalised outreach drafted for a human to approve and send.
Compliance review
Documents checked against policy, exceptions flagged with citations, and evidence packaged for audit.
Data operations
Pipeline failures diagnosed, schema drift identified and fixes proposed with the diff attached.
The agent stack we run
Reasoning
- Frontier LLMs
- Self-hosted open models
- Model routing
- Structured outputs
Orchestration
- Durable execution
- Planner and executor patterns
- Tool registries
- Multi-agent coordination
Memory
- Vector and hybrid retrieval
- Episodic task memory
- Knowledge graphs
- Context compaction
Safety
- Permission scoping
- Spend and step limits
- Trace logging
- Evaluation harnesses
Trusted to ship AI across the world's operational industries
ManufacturingLogisticsEnergyHealthcareFinanceSmart CitiesAgricultureRetailAerospaceTelecomMiningPublic SectorManufacturingLogisticsEnergyHealthcareFinanceSmart CitiesAgricultureRetailAerospaceTelecomMiningPublic Sector
Questions we get asked about ai agents
What is an AI agent?
An AI agent is software that pursues a goal over multiple steps. It plans, calls tools such as APIs, databases or browsers, observes what came back, and adapts until the task is complete or it hits a limit. The difference from a chatbot is that an agent acts, and the difference from an automation is that the sequence is decided at run time rather than fixed in advance.
When should we not use an agent?
When the process is stable and rule-based. If a deterministic workflow can do the job, it will be cheaper, faster and easier to debug. We routinely recommend a plain automation instead of an agent, and we will say so during scoping.
How do you stop an agent doing something harmful?
Tools are scoped to least privilege, irreversible actions require human approval, and every run is bounded by step, time and spend limits. Everything is traced, so any decision can be replayed and audited after the fact.
How do you know an agent actually works?
We benchmark it against real historical tasks with known outcomes and report task completion rate, human intervention rate and cost per task. Those numbers gate the rollout and are tracked continuously afterwards.
Can agents run on our own infrastructure?
Yes. We deploy into your cloud account or on-premise, including with self-hosted open models where data residency or regulation requires that nothing leaves your network.
Related capabilities
Let's build
Have an idea? We'll ship it.
Tell us the problem. We'll come back with an architecture, a timeline and a team ready to build it in production.