AI Agents
Unlock the full potential of your business with agentic AI systems
From tasks assigned to work completed, without a person checking every step
We help you to:
Map use cases, where agents and multi-agent systems will create the most value
Stress-test agents against real scenarios before they reach production
Design agents that reason, act, and coordinate with each other where the work needs it
Monitor and improve performance as agents scale into full systems
Agentic AI Services
Strategy
Agent & System Opportunity Mapping
A structured review of your products and workflows to identify where agents create the most value, before any build starts.
Design
Agent Prototyping & Validation
Agent and system behaviour stress-tested against real business scenarios before anything reaches production.
Build
Multi-Agent Orchestration
For complex processes: agents that divide the work, hand off tasks, and stay accountable to each other rather than operating as separate bots.
Build
Workflow Automation
Repetitive, multi-step processes automated end to end through predefined, deterministic steps, with agents brought in where a step genuinely needs judgement.
Build
Knowledge & Retrieval Agents
Internal knowledge structured so agents decide what to look up and surface the right answer instead of a guess.
Build
Tool & System Integration
Agents that are connected to the real systems and APIs they need to act on, so they enhance existing processes instead of sitting outside them.
Build
Local Agent Engineering
Engineering the guardrails, orchestration, and integration layer around Claude Code, powered by Anthropic, and other local coding agents, so they hold up across a team rather than a single developer's machine.
Scale
Agent Monitoring & Performance
Tracks agent behaviour in production and tunes it continuously, so performance holds up as usage and scope grow.
Building agentic systems is only half the job. Making the agents hold up in production is the other half.
Most organisations are already running LLMs and agents in some form, either built in-house or through tools like Claude Code. The failure modes differ depending on which one you're looking at. Custom systems hallucinate, lose context, or reason inconsistently once they leave the prototype stage. Local coding agents work well for one developer but were never built for team-wide, enterprise-scale use.
We design and build LLM-based and agentic systems from the ground up, from a single well-scoped agent to multi-agent systems that reason, act, and hand off work to each other.
We also engineer the guardrails, orchestration, and integration layer around Claude Code, and other large LLM models. Our experts build systems that hold up across a team and connect properly to your real systems.
Both draw on the same research base. We run ongoing R&D, internally and with university partners, into the failure modes that actually break agents (false claims, context rot, drift) and LLM-based systems (hallucination, weak retrieval, faulty reasoning, poor calibration, non-determinism).
Every system we build is measured against our Trust Score, the model layer of the framework below, before it goes near production.
Our Approach
How our trust architecture framework keeps systems scalable
Trust & Control Framework
The trust layer for agentic systems in production
Our proprietary framework for building agentic systems your teams and auditors can actually rely on.
Trust Architecture secures four layers of enterprise trust: Provenance, Confidence, Consistency, and Attribution, and analyses model behaviour directly to catch hallucinations and reasoning errors before they reach production.
Build agents engineered to prove their reasoning, from deterministic workflows to autonomous multi-agent swarms.
What you get:
- Full provenance trail on every fact and generated claim
- Calibrated confidence scores
- Automatic detection of conflicting sources
- Ontology-aware agents built on your real business logic
Use cases
In action
What AI Agents unlock once they're running effectively
FAQs: AI Agents
What are AI agents, and how can they help my business?
AI agents are automated systems designed to handle complex workflows, process data, and make decisions. They can automate repetitive tasks, improve knowledge retrieval, and enhance business operations.
Can AI agents be customised to my specific needs?
Yes, we build customised AI agents tailored to your industry and business requirements. Whether you need assistance with customer support or process automation, we develop AI agents that fit your unique needs.
What types of tasks can AI agents automate?
AI agents can automate a range of tasks, from data processing and customer service enquiries to knowledge retrieval and workflow management. They enhance efficiency by handling repetitive and complex tasks, freeing up your team for strategic work.
Can your AI agents integrate with our existing software and platforms?
Yes, we design AI agents to integrate seamlessly with your existing software ecosystem, whether it involves CRM systems, ERPs, or custom platforms. Integration ensures that the AI agent enhances, rather than disrupts, current workflows.
How can AI agents improve knowledge insights within my organisation?
AI agents can sift through large volumes of data to identify patterns, trends, and insights, providing you with actionable knowledge. They streamline data retrieval and can offer real-time recommendations, aiding in more informed decision-making.
How do AI agents differ from traditional automation tools?
Unlike traditional automation tools, AI agents can make decisions, adapt to changing data inputs, and improve their performance over time. They leverage AI techniques like machine learning and natural language processing to offer more dynamic solutions.
How do you validate the performance of an AI agent?
We conduct rigorous validation processes, including simulated testing and real-world performance monitoring, to ensure the AI agent operates reliably. Validation includes testing for accuracy, security, and alignment with specified business goals.
What kind of ongoing support do you offer for AI agents?
We provide post-launch support that includes monitoring, maintenance, and updates. This helps keep your AI agents running smoothly, with performance adjustments and security updates as needed to adapt to evolving business needs.
How do you sustain the security and reliability of AI agents?
We design security and reliability into agents from the first build, then hold them in place through governance and monitoring once they are live. Our approach combines:
- Trust Architecture: our proprietary framework secures four layers of enterprise trust: Provenance, Confidence, Consistency, and Attribution. It analyses model behaviour directly to catch hallucinations and reasoning errors before they reach production.
- Z-Forge: our agentic harness with trust score, giving each agent a measurable reliability rating before it goes near production.
- Z-CORA: our proprietary Claude plugin family, adding trust and control to agent workflows through human-in-the-loop checkpoints.
- ATOM: our AI Trust Operating Model sets ownership and approval paths for AI decisions, so accountability is clear as agents take on more work.
Data protection: encryption, authentication, and access controls, with sensitive data handled in line with GDPR and other privacy frameworks.
In production, step-level tracing, timeouts, fallbacks, and incident playbooks turn a failure into an alert your team can act on. No agent is immune to hallucination, so we build each system to catch errors and fix or escalate them to a person rather than hide them.