Agentic workflow systems
Single-agent, planner-executor and multi-agent systems that coordinate complex work while preserving human authority.
We design, build and hand over secure AI agents for financial and automotive businesses—combining reasoning, enterprise knowledge and deterministic execution.
We turn business workflows into controlled agentic systems that can reason, retrieve knowledge, use tools and execute within explicit boundaries.
Single-agent, planner-executor and multi-agent systems that coordinate complex work while preserving human authority.
Grounded assistants that retrieve trusted organisational knowledge with citations, access controls and measurable answer quality.
Secure cloud foundations with policy enforcement, audit trails, observability, cost controls and deterministic execution services.
From secure tool connectivity to autonomous execution, each capability is engineered as part of one governed system.
Single-agent, supervisor, planner-executor and multi-agent architectures aligned to the workflow and risk.
Secure Model Context Protocol servers that expose enterprise tools, data and actions through controlled interfaces.
End-to-end agent execution for approved low-risk processes, with bounded permissions, validation and recovery paths.
Approval gates, exception queues and escalation paths for sensitive, uncertain or high-impact decisions.
Permission-aware retrieval across trusted knowledge with grounding, citations, evaluation and freshness controls.
Policy enforcement, audit trails, prompt and tool tracing, quality metrics, latency monitoring and cost controls.
Every request passes through identity, policy, knowledge and execution controls. The agent never receives unrestricted access to enterprise systems.
User intent and business context enter the system.
Permissions, data access and allowed actions are checked.
The agent decomposes the goal into controlled steps.
Trusted knowledge is retrieved with access controls.
MCP servers expose only approved tools and data.
High-impact actions pause for human review.
Validated actions run with complete traceability.
We separate probabilistic AI reasoning from retrieval, policy decisions and business execution—so the system remains safe, testable and auditable.
We design, deploy and operate AI systems to remain trustworthy, safe, compliant and aligned with human and regulatory expectations.
Test for systematic bias across user groups, datasets and outcomes.
Provide traceable evidence for model outputs, retrieved sources and actions.
Protect training and inference data through access control, encryption and minimisation.
Prevent harmful, toxic or unsafe outputs using layered controls and testing.
Bound agent behaviour through permissions, policies, approvals and shutdown controls.
Measure accuracy and resilience against noisy, unexpected or adversarial input.
Apply ownership, policies, audit evidence, change control and lifecycle management.
Disclose AI usage, limitations, data sources and when human oversight applies.
Every agent starts with a clear business outcome, authority boundary and measurable acceptance criteria.
Map the workflow, users, knowledge, decisions, tools, risk and expected value.
Define agent roles, permissions, guardrails, human approvals and failure paths.
Engineer the system and test quality, security, latency, cost and resilience.
Launch with observability, documentation, source code and knowledge transfer.
Designed for industries where accuracy, security, traceability and operational performance are non-negotiable.
Agentic research, knowledge, operations and decision-support systems with strict data, risk and human-control boundaries.
An agentic parts intelligence system combining exact part lookup, semantic knowledge retrieval and controlled AI responses.
Open live demo ↗Share the workflow, users, data sources and required controls. We will identify a practical route from use case to production.
Email gourav@gouravgarg.co.uk ↗