Assistant
Searches, analyses, drafts and explains the next step without changing operational systems.
Agentic AI · Virtek
AI agents that do more than answer —
they get work done
We design agentic systems that understand a goal, assemble context, plan steps and act safely across enterprise systems. Models, knowledge, APIs and people become one governed digital process.
From process hypothesis to production operations and AgentOpsBeyond the chatbot
We do not add AI for effect.
We redesign how work is executed
A chatbot produces an answer. An agentic system manages task state, selects permitted tools, validates conditions and brings a process to a controlled outcome. Autonomy is set by the cost of error, reversibility and data maturity.
Workflow agent
Executes a sequence through APIs and tools, follows rules and requests approval for consequential actions.
Agent system
Specialist agents divide work, pass context and operate under a shared orchestrator and policies.
Reference architecture
An agent is not a model.
It is an engineered system around one
Reliability comes from layers. Every component can be observed, evaluated, constrained and replaced independently, so the business process is not locked to one model provider.
Goal and routing
Determine intent, context, permitted route and the task completion criterion.
Planning and orchestration
Break down work, choose the sequence, handle exceptions and manage retries.
Model gateway
Route between private and cloud models by quality, latency, cost and data policy.
Knowledge and memory
Connect RAG, databases, graphs and short-term memory with permissions, freshness and provenance.
Tools and integrations
Expose well-described APIs, MCP tools, queues and automations rather than uncontrolled system access.
Identity and authority
Act for a named user or service role with least privilege, limits and explicit ownership.
Validation and approval
Data schemas, business rules, human-in-the-loop, safe failure and expert handoff are part of execution.
Tracing and evaluation
Capture versions, tool calls, latency, cost, failures and outcomes for every run.
Deterministic steps remain conventional code and workflow automation. A model is used only where interpretation, retrieval, planning or unstructured context genuinely requires it.
Where agents create value
Automating end-to-end work,
not individual answers
We begin with a process that has a clear input, owner, permitted actions and measurable outcome. Then we decide what the agent handles, what remains deterministic automation and what stays with a person.
IT operations and Service Desk
Classify requests, gather telemetry, search knowledge, prepare diagnostics, run an approved playbook and escalate to an engineer.
Documents and enterprise knowledge
Parse incoming documents, check completeness, extract fields, compare with policy and produce evidence-linked output.
Procurement and supply
Capture demand, compare offers, check constraints, prepare approvals and synchronise records with the system of record.
Sales and customer work
Qualify requests, assemble CRM context, prepare the next action, content and tasks while retaining the decision history.
Engineering and software delivery
Analyse requirements, work with repositories, prepare changes, check quality, maintain documentation and support incident analysis.
Back office and employee services
HR, contract, finance and administrative requests with role-aware data, approval routes and expert handoff for exceptions.
How we deliver
From process map
to a governed digital worker
A PoC must prove task completion, not merely produce an attractive conversation. Production begins only after quality, security, economics and operating readiness have been evaluated.
01
DISCOVERUnderstand the process
Observe the work and capture exceptions, manual judgements, systems, data and failure impact.
02
DESIGNDesign the agent role
Define the goal, authority, tools, memory, approvals and stop conditions.
03
CONNECTPrepare integrations
Build secure adapters for APIs, databases, queues, files and interfaces and normalise context.
04
EVALUATEBuild evaluations
Collect real scenarios, edge cases, adversarial checks and business outcome criteria.
05
PILOTRun a bounded pilot
Test with real load, telemetry, limits, approvals and a rapid rollback path.
06
OPERATEMove into operations
Establish SLOs, tracing, cost controls, support, versioning, regression tests and a roadmap.
Architectural precision
Not the most complex approach —
the most dependable one
Deep capability is shown by the right boundary between software, model, person and enterprise system, not by the number of agents.
Agent or conventional automation?+
If the route is fully known, use a deterministic workflow. Add an agent for contextual interpretation, variable planning and tool selection.
One agent or many?+
Start with the smallest architecture. Split agents when roles, permissions, models, contexts or quality criteria must be independent.
API, MCP or user-interface operation?+
Prefer stable APIs and typed tools. Use MCP for a governed capability catalogue; computer use only when an integration is unavailable.
Private or cloud model?+
Choose by data, latency, quality, cost and residency. We support private, cloud and hybrid routing.
Does the agent need long-term memory?+
Only for a proven use case. Memory needs ownership, lifespan, provenance and rules for correction and deletion.
When may the agent act autonomously?+
Increase autonomy gradually: observe, recommend, act with approval, then permit bounded execution of reversible operations.
What the customer receives
Not a demo, but
a durable business capability
We deliver more than an interface. The solution includes architecture, integrations, evaluation assets, operating rules and enablement for the customer team.
Process map and value model
Baseline, bottlenecks, manual effort, target measures and stop criteria.
Agent-system architecture
Agent roles, models, data, memory, tools, trust boundaries and interaction diagrams.
Integration layer
Versioned API adapters, MCP services, queues, schemas, secrets and access policies.
Production agent runtime
Experience, runtime, orchestration, model gateway, context stores and safe tool execution.
Evaluation and acceptance
Real tasks, negative cases, regression, load checks and a reproducible report.
AgentOps and handover
Dashboards, logs, SLOs, versioning, runbook and training for users and engineers.
AgentOps
Manage the quality
of every execution
We measure the system's ability to complete a business task consistently, not the abstract intelligence of a model. Technical telemetry is tied to process outcomes.
Task completion
Share of scenarios reaching the correct outcome without a hidden process violation.
Action accuracy
Correct tool selection, call parameters, sequence and error handling.
Context grounding
Source correctness, permission compliance, freshness and evidence sufficiency.
Human involvement
Where and why approval, correction, escalation or full manual handling was required.
Speed and reliability
Stage latency, integration errors, retries, availability and time to recovery.
Run economics
Cost per task, tokens, compute, external calls and the actual process impact.
Safety and policy
Policy violations, blocked actions, sensitive-data exposure and guardrail activations.
Regression and drift
Quality changes after updates to models, prompts, tools, data or routing.
Technology system
Connecting models, data
and enterprise systems
The architecture stays portable: business logic, tools and evaluations are separated from a specific model and hosting environment.
Models and routing
- Private and cloud LLMs
- Multimodal and reasoning models
- Model gateway and fallback
- Quality, latency and cost controls
Context and memory
- RAG and hybrid retrieval
- Vector and graph stores
- Source-level permissions
- Short- and long-term memory
Actions and integrations
- OpenAPI and typed functions
- MCP servers and tool catalogue
- Queues, events and workflows
- Sandboxed code and task execution
Control and operations
- SSO, service roles and secrets
- Human-in-the-loop and policies
- Tracing, evals and AgentOps
- CI/CD, versions and safe rollback
We can enter at any level: assess a process, design the platform, build the first agent, integrate an existing solution or establish a shared foundation for an agent portfolio. Our team combines software, data, infrastructure, cybersecurity and operations so the agent does not remain an isolated lab feature.
Start with the process
Show us where work stalls.
We will design the agent around the outcome
In the first session we map one process, systems, data, exceptions and the cost of error. We then propose a pilot boundary, architecture and measurable success criteria.
Discuss the challenge↗︎