AI & Agentic Systems
Agents that do real work, under real permissions
The gap between an impressive AI demo and a system an enterprise can run is architecture: tool layers, permission tiers, validation, observability. That is the part we build.
Position
RPA taught us what agents must not do
Years of unattended automation at enterprise scale are a long education in failure modes: selectors that drift, sessions that expire, systems that lie about their state. Agentic AI inherits every one of those problems and adds a new one - a component that can be confidently wrong.
We build agent systems the way we build robots: capabilities behind explicit contracts, authority behind explicit permissions, and every consequential decision either validated deterministically or routed to a human. The LLM is a reasoning engine inside an engineered system - never the system itself.
Currently engaged building background agents and LLM chat capabilities for an enterprise AI-agent marketplace at a global healthcare-technology enterprise.
Architecture
The shape of a production agent system
What we deliver
Three engagement patterns
Background agents
Unattended agents that watch queues, process documents, run research and execute multi-step tasks - with heartbeats, retries and escalation paths designed like the RPA fleets they replace.
LLM chat in your workflow
Chat surfaces wired to your systems through governed tool layers - so answers come from your data and actions land in your systems, with an audit trail. Copilot-style extensions where your teams already work.
LLM-assisted delivery
Using models to build faster without shipping their mistakes: knowledge-injection specs, scaffolding, static validators that catch hallucinated code before a human ever reviews it.
$ python uipath_validate.py --project ./GeneratedProcess Process.xaml:214: ERROR UNDECLARED-VAR reference 'dtResults' is never declared Process.xaml:389: ERROR INVOKE-CONTRACT 'GetEquipment.xaml' expects In 'Input' (Dictionary) — not passed Main_Entry.xaml:41: WARN UNUSED-VAR 'strTemp' declared but never referenced 2 errors, 1 warning -> findings returned to the model for a targeted retry $ python uipath_validate.py --project ./GeneratedProcess # pass 2 0 errors, 0 warnings — Studio-ready
Illustrative excerpt. Output format, rule classes and the retry loop are exactly as delivered.
Evidence
Case studies from this practice
Applied AI / Intelligent Automation
Teaching a local LLM to write production UiPath code
A knowledge-injection spec plus a static-analysis toolchain lets a locally hosted open-weight model generate UiPath XAML that passes validation before Studio ever opens - with no client code leaving controlled infrastructure.
Read the case study
Applied AI / Document Intelligence
AI validation of US tax-exemption certificates across seven state form families
Azure Document Intelligence OCR feeding a deterministic rule engine - 8 generic checks plus 65+ state-specific rules - triages certificates into pass, fail or human review with per-rule evidence.
Read the case study
Private AI / Infrastructure
A private AI platform with zero external AI APIs
Dual-backend LLM inference, 30+ agent tools behind tiered auth, voice, image generation and self-healing infrastructure - a complete AI platform where no prompt, document or token ever leaves the network.
Read the case study
- MCP
- Background agents
- LLM chat
- Tool design
- Auth tiers
- Knowledge injection
- Static validation
- Open-weight models
Considering agents for actual operations?
Tell us which workflow you want an agent inside and what it must never be allowed to do. Both halves of that sentence get equal engineering.