AI agents that finish the work, not just the demo.
We engineer production-grade AI agents and multi-agent systems that reason over your data, call your tools through secure integrations, and hand off to people at the right moments. Every agent ships with evaluation suites, guardrails, observability and a clear owner.
6–10 wks
Typical pilot-to-production window
100%
Agent actions traced and auditable
Human-in-the-loop
Approval gates on every high-risk action
What is agentic AI?
Agentic AI refers to systems built on large language models that can pursue a goal autonomously: they break a task into steps, decide which tools or data to use, take actions in other software, check their own results and escalate to a human when confidence is low. Unlike a chatbot that only answers questions, an AI agent completes work, such as triaging a claim, reconciling an invoice or resolving a support ticket end to end.
Design, build and operate AI agents that plan, use tools and complete multi-step work across your systems, with guardrails, evals and human oversight built in.
Our Agentic AI capabilities.
Agent architecture & design
Task decomposition, autonomy levels, planner/executor patterns and when not to use an agent at all.
Multi-agent orchestration
Supervisor, swarm and graph-based agent topologies with A2A (agent-to-agent) communication and shared memory.
MCP servers & tool integration
Secure Model Context Protocol servers that expose your ERP, CRM, ticketing and data APIs to agents with scoped permissions.
Evals & quality engineering
Golden datasets, LLM-as-judge and task-success evals wired into CI so every prompt or model change is measured before release.
Guardrails & safety
Prompt-injection defence, PII redaction, policy checks, action allow-lists and approval workflows for sensitive operations.
AgentOps & observability
OpenTelemetry tracing, cost and latency budgets, drift alerts and runbooks so agents are operated like any critical service.
Where it pays back first.
High-value starting points we see across industries. We will help you rank them against your own data, systems and risk appetite.
01
Customer operations
Agents that resolve tickets, issue refunds within policy and draft responses for review.
02
Finance & procurement
Invoice matching, vendor onboarding, spend-policy checks and month-end reconciliation.
03
Insurance & lending
Claims intake, document verification, underwriting pre-checks and fraud signals.
04
IT & engineering
Incident triage, runbook execution, code review assistance and change-risk scoring.
05
Sales & revenue
Account research, RFP response drafting, CRM hygiene and pipeline follow-ups.
06
Knowledge work
Research agents that search, cite and synthesise across internal documents and the web.
A clear path to production.
Discover
Map one high-value workflow, its systems, risks and success metric.
Design
Choose autonomy level, tools, guardrails and the human hand-off points.
Build & evaluate
Ship the agent behind evals, red-team it, and iterate on real cases.
Operate
Release gradually, monitor quality and cost, and expand to adjacent workflows.
Tools we ship with.
Selected on merit for your context. We are model-, cloud- and vendor-agnostic.
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Model Context Protocol (MCP)
- A2A
- LangGraph
- Claude Agent SDK
- OpenAI Agents SDK
- Google ADK
- CrewAI
- Microsoft Agent Framework
- Temporal
- Langfuse
- LangSmith
- Arize Phoenix
- OpenTelemetry GenAI
- AWS Bedrock
- Azure AI Foundry
- Vertex AI
Agentic AI: your questions answered.
We are model-agnostic. We benchmark frontier models such as Claude, GPT and Gemini alongside open-weight models like Llama and Mistral against your own tasks, then choose on accuracy, latency, cost and data-residency requirements. Architectures are designed so models can be swapped as the market moves.
Agents run with least-privilege tool access, action allow-lists, human approval on high-risk steps, prompt-injection and PII filters, and full tracing of every decision. Releases are gated by evaluation suites, and we red-team agents before go-live.
The Model Context Protocol (MCP) is an open standard for connecting AI models and agents to tools and data sources. Building MCP servers for your systems once lets any compliant agent or assistant use them securely, avoiding one-off integrations for every new AI use case.
A focused, single-workflow agent typically reaches a production pilot in six to ten weeks, depending on system access and data readiness. We start with a two-week discovery to pick the workflow with the clearest ROI.
Ready to put Agentic AI to work? Start with one workflow.
Book a 45-minute strategy session with a Vidura principal. You'll leave with a shortlist of high-ROI AI use cases, the risks to manage, and a realistic path to production.