AI agents that don't just talk. They run processes end-to-end.
We design and deploy a digital workforce wired into your systems: multi-agent orchestration, low-latency voice, MCP connectivity and deterministic execution with human approval where it matters.
Why the old approach breaks
Chatbots and rigid no-code flows were step one. Production agents are a different engineering discipline.
Legacy chatbots & rigid flows
- Passive: wait for questions and return text.
- Rigid flows stop at the first unexpected case.
- Free-form prompts and hallucination risk.
- Black box — no idea why a decision was made.
RoboSync agent systems
- Action-oriented: write to APIs, databases and ERPs.
- Plan, reason and recover from exceptions autonomously.
- Deterministic guardrails: typed schemas and business-rule validation.
- Full observability: every step, tool call and cost traced.
Four agent architectures
Pick the job. We engineer the system that does it.
Autonomous operations agents
Take repetitive operations end-to-end: order intake, reconciliations, multi-system sync, contract generation.
Native tool calling · sandboxed execution · transactional rollback
Real-time voice & front-desk agents
Natural two-way phone calls that book appointments, triage requests or qualify leads while querying your data live.
Speech-to-speech models · SIP / telephony · live data lookup
Document & data intelligence
Compare thousands of pages, extract complex tables with vision models and validate data against your standards.
Hybrid search · knowledge graphs · vision models · strict schemas
Multi-agent orchestration & copilots
Specialised agents collaborate: one researches, one analyses, one validates business rules, one writes the result.
Agent graphs · MCP servers · long-term memory
Where agents pay off first
The processes we automate most often — now run by agents instead of brittle scripts.
A production-grade stack
No toy tools. Model-agnostic, observable and fully transferable to you.
Orchestration
Agent graphs and stateful workflows in TypeScript and Python.
Connectivity
Model Context Protocol (MCP), REST/GraphQL, browser agents for legacy UIs.
Reasoning models
Latest frontier models from OpenAI, Anthropic and Google, plus private open-weight models for sensitive data.
Guardrails
Structured outputs, policy checks and approval gates on critical actions.
Observability & evals
Tracing, cost tracking and automated evaluations that catch quality drift.
Deployment
Your cloud, our cloud or on-premise — with full source and documentation handed over.
Enterprise safety by design
Your data stays yours
Enterprise model agreements with zero data retention and no training on your data.
Human-in-the-loop
For payments or critical changes, the agent prepares everything and asks for one-click approval in Slack, Teams or WhatsApp.
100% audit trail
Every decision, tool call and parameter is logged and reviewable.
From process to autonomous agent
Four phases, measurable at every step.
Process & feasibility
Map the workflow, list edge cases and build the evaluation set.
Architecture & guardrails
Build tools, connect systems and write automated evals.
Shadow mode
The agent runs alongside your team without final say, so we calibrate accuracy.
Controlled autonomy
Autonomous execution with exception alerts and a live monitoring dashboard.
AI agents — frequently asked questions
What is an AI agent?
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An AI agent is software that uses a language model to plan steps, call tools (APIs, databases, ERPs, email) and complete a task end-to-end — not just answer questions. It acts, checks the result and asks a human for approval when a decision is critical.
How do I build an AI agent for my business?
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1) Pick one repetitive process with clear inputs and outputs. 2) Map the steps, the systems involved and the edge cases. 3) Give the agent tools — typed connections to your APIs or MCP servers. 4) Add guardrails: structured outputs, business-rule validation and human approval for critical actions. 5) Build an evaluation set from real examples. 6) Run it in shadow mode next to your team, then switch on controlled autonomy with monitoring.
How long does it take to build an AI agent?
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A focused production agent for one process typically takes a few weeks from feasibility to shadow mode. Complex multi-agent systems connected to several internal tools take longer. We send a fixed-scope proposal within 24h.
What is the difference between an AI agent and a chatbot?
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A chatbot answers questions with text. An AI agent executes work: it reads documents, writes to your systems, handles exceptions and logs every action for audit.
Is my data safe with an AI agent?
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Yes, when built correctly. We use enterprise model agreements with zero data retention, private open-weight models for sensitive data, role-based access and a full audit trail. Agents can also run in your own cloud or on-premise.
What is MCP (Model Context Protocol)?
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MCP is an open standard that lets AI agents connect to tools and data sources in a consistent, secure way. We use it to plug agents into CRMs, ERPs, document stores and internal APIs.
Ready to put an agent to work?
Tell us the job. We come back with a fixed-scope proposal within 24h.