Organisations automating knowledge-heavy workflows
Use agents to gather context, analyse, draft, recommend, route, and execute approved steps across existing systems.
Tool use, workflow orchestration, approvals, policy, memory, and observable autonomy
AI agent development
Rokad builds AI agents that reason over context, use approved tools, coordinate workflows, and operate within explicit permissions, policies, and human controls.
Designed for / 01
A focused delivery model for the organisations that need it.
Useful agents must do more than generate text. Rokad engineers agent systems with task boundaries, tool contracts, orchestration, state, retrieval, permissions, approval gates, evaluation, tracing, recovery, and operator oversight for dependable work inside real products and operations.
Software companies adding agent capabilities
Embed controlled tool use, multi-step work, memory, and human collaboration inside a product.
Teams replacing brittle prompt automations
Introduce explicit state, tools, policies, evaluation, observability, and recovery around AI-driven workflows.
Challenges / 02
The problems this service is built to solve.
The agent appears capable but is not dependable
Unbounded prompts, inconsistent context, hidden state, and weak tool contracts create unpredictable behaviour.
Actions carry operational or financial risk
The system needs permissions, policy evaluation, approval, audit, idempotency, and safe recovery before side effects.
Failures are difficult to diagnose
Teams cannot inspect decisions, retrieval, tool calls, model behaviour, cost, latency, or workflow state.
Capabilities / 03
What Rokad can deliver.
01
Single-agent and multi-agent workflow architecture
02
Tool schemas, connectors, API actions, and controlled execution
03
State, plans, memory, retrieval, context, and task decomposition
04
Permissions, policy checks, approvals, budgets, and action limits
05
Human review, escalation, exception, retry, and recovery workflows
06
Evaluation, simulation, tracing, audit, cost, and performance controls
07
Product interfaces, operator consoles, deployment, and managed operation
Solution components / 04
The system behind the visible product.
Agent runtime
Task state, planning, context, model routing, tool selection, execution, observation, and completion behaviour.
Tool and policy layer
Typed actions, permissions, preconditions, side-effect controls, approval, budgets, and audit evidence.
Knowledge and memory
Retrieval, working context, durable state, user preferences, organisational knowledge, and retention rules.
Operations and evaluation
Traces, datasets, simulations, quality metrics, failures, costs, latency, versions, and operator intervention.
Use cases / 05
Where this capability creates practical leverage.
Research and analysis agent
Collect governed evidence, compare sources, synthesise findings, identify uncertainty, and prepare reviewable outputs.
Customer operations agent
Interpret requests, gather account context, recommend or execute approved actions, and escalate exceptions.
Engineering and operations agent
Inspect systems, prepare changes, run approved diagnostics, coordinate tools, and maintain an auditable work record.
Back-office workflow agent
Process documents, validate data, update systems, create drafts, route approvals, and monitor completion.
Architecture and integration / 06
Designed to fit the wider technology environment.
Bounded autonomy
Define what the agent may decide, what requires deterministic validation, and what must remain under human authority.
Tool contracts
Use explicit schemas, permissions, preconditions, outputs, error semantics, idempotency, and compensating actions.
Durable execution
Persist state and events so long-running work can pause, resume, retry, escalate, and survive infrastructure failure.
Quality and control / 07
Production requirements are part of the build.
Measured behaviour
Representative evaluation data, quality criteria, failure modes, and release thresholds are defined before expanding production use.
Controlled actions
Permissions, policy checks, approval gates, audit trails, fallbacks, and escalation paths govern consequential AI behaviour.
Observable operation
Inputs, outputs, retrieval, tool calls, latency, cost, model versions, and quality trends are monitored appropriately.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the business outcome, users, workflows, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the system boundaries, data, integrations, security, operating model, delivery sequence, and technical decisions.
Build and validate
Deliver in controlled increments with stakeholder review, automated testing, documentation, and production-quality engineering.
Deploy and improve
Launch safely, establish observability and support, then improve the system using operational evidence and user feedback.
Typical deliverables
Agent feasibility, task, risk, and control assessment
Agent, tool, state, knowledge, and policy architecture
Production agent runtime, interfaces, and integrations
Permissions, approvals, budgets, audit, and recovery controls
Evaluation datasets, simulations, quality thresholds, and traces
Deployment, monitoring, operator, and governance documentation
Engagement models / 09
Use the delivery structure that matches the work.
Fixed-scope delivery
A defined outcome, scope, acceptance criteria, milestones, and commercial structure for a bounded project.
Dedicated product team
A stable cross-functional team delivering an evolving roadmap with shared product and engineering ownership.
Embedded specialists
Specialist engineers working inside an existing product, technology, data, design, or operations team.
Managed evolution
Ongoing reliability, security, maintenance, feature delivery, and roadmap execution after launch.
Related capabilities / 10
Continue through the wider product and technology system.
01
RAG and knowledge systems
Ground agent decisions in governed enterprise information.
02
AI integration
Connect agents with existing products, workflows, data, and applications.
03
MLOps
Operate models, evaluations, versions, observability, and release controls.
01
Software development
Custom platforms, backends, integrations, operational systems, and software modernisation.
02
Managed technology services
Ongoing maintenance, cloud, security, reliability, support, and continuous engineering.
03
Technology consulting and research
Architecture, feasibility, strategy, due diligence, vendor evaluation, and execution planning.
FAQ
AI agent development
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01How much autonomy should an AI agent have?
How much autonomy should an AI agent have?
Autonomy should follow action risk, reversibility, evidence quality, user expectation, and organisational policy. We separate low-risk assistance, reviewable recommendations, approved actions, and tightly bounded autonomous execution.
02Can agents use our existing software and APIs?
Can agents use our existing software and APIs?
Yes. We expose approved capabilities as typed tools with authentication, permissions, validation, limits, audit, and failure handling rather than giving the model unrestricted system access.
03How do you prevent agents from taking unsafe actions?
How do you prevent agents from taking unsafe actions?
Controls can include tool allowlists, scoped credentials, policy checks, approval gates, amount and frequency limits, deterministic validation, sandboxing, idempotency, audit, and escalation.
04Can a human review or take over a task?
Can a human review or take over a task?
Yes. Workflows can pause for approval, request missing information, surface evidence, transfer state to an operator, and resume after a decision.
05How are agent improvements tested?
How are agent improvements tested?
We use representative tasks, simulations, regression datasets, tool-call assertions, policy tests, human review, production sampling, and controlled model or prompt releases.
AI development
Build agents that can work inside real operational boundaries.
Rokad will define the tasks, tools, authority, controls, evaluations, and operating model before increasing autonomy.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.