AI agent development for the work your team shouldn't be doing by hand
AI agent development is the engineering of software agents that use large language models to plan, call tools and complete multi-step tasks with limited human input. Sysvoy builds production agents and multi-agent systems — from a single agent that drafts and files documents to an orchestrated team of agents that researches, triages and acts inside your existing systems — with the evaluation, guardrails and monitoring needed to trust them in production.
One task, start to finish
An orchestrator splits the job, specialised agents work through your tools, and the uncertain cases go to a person.
- taskCheck 120 supplier contracts for auto-renewal terms
- planOrchestrator splits the job across four agents
- reader120 PDFs parsed from Documents
- analyst23 auto-renewal clauses found
- checker2 unclear clauses sent to a person
- writerSummary with 23 citations, CRM updated
- done4 min 12 s, every step logged
Where AI agent development projects stall
And what we do differently.
Demo-ware that never ships
A prompt chain that works on ten examples fails on the eleventh. We build the evaluation set first and don't ship until the agent passes it.
Too much autonomy, too soon
Agents with broad permissions make expensive mistakes. Ours start narrow, with approvals, and earn autonomy as results prove out.
Nobody can see what it did
Without traces, every failure is guesswork. Each step our agents take is logged and reviewable.
What we build
Workflow agents
Agents that run a defined process end to end — intake, research, drafting, filing — and hand off to a person when confidence is low.
Multi-agent systems
An orchestrator that splits work across specialised agents, checks each result and assembles the final output.
Document agents
Review, extraction and comparison across contracts, clinical and legal documents, with every claim linked back to its source page.
Customer-facing assistants
Assistants connected to your catalogue, CRM or knowledge base — like the self-hosted commerce assistant we built for a US B2B distributor.
Back-office agents
Agents that reconcile records, update CRMs such as Salesforce and HubSpot, and keep systems in sync without manual re-entry.
How we deliver
From the first workshop to production. How we work
One workflow first
We map a single task, agree what a correct and an incorrect result look like, and build the evaluation set before the agent.
Tools, not just prompts
Agents act through scoped APIs with permissions you control, so they can only do what a well-briefed employee could.
People in the loop by design
Approval steps, confidence thresholds and full audit logs are part of the first release, not an afterthought.
Measured in production
Tracing, cost and latency tracking, and regression tests on every change keep the agent reliable as models and data shift.
AI agents compared with rule-based automation and chatbots
| Rule-based automation | Chatbot | AI agent | |
|---|---|---|---|
| Handles unstructured input | No | Partly | Yes |
| Takes actions in your systems | Yes, fixed steps | Rarely | Yes, chosen per task |
| Copes with exceptions | Breaks | Hands off to a person | Reasons, then escalates if unsure |
| Best for | High-volume identical tasks | Answering common questions | Multi-step work that needs judgement |
A good fit when
- You have a repeatable process that is heavy on documents, research or data entry.
- Your data has to stay inside your own cloud or servers.
- You built a prototype that impresses in demos but isn't reliable enough for real users.
What we build with
- Python
- TypeScript
- Node.js
- Commercial and open-weight LLMs
- Vector and hybrid search
- Typesense
- Salesforce & HubSpot APIs
- AWS
- Google Cloud
- Self-hosted deployment
Related work
AI security platform
AI-driven security platform and SOC tooling for a European cybersecurity company.
Client under NDAAI commerce assistant
Self-hosted AI shopping assistant, Typesense product search and HubSpot integration for a US B2B distributor.
US distributormidpage.ai
AI research platform that layers analysis over case law for litigators and legal teams.
midpage.ai ↗
Common questions
Not answered here? Email contact@sysvoy.com.
How long does it take to build an AI agent?
A pilot for one well-defined workflow is the fastest route to value. The timeline depends mostly on how many systems the agent must connect to and how strict accuracy needs to be, so we scope it with you in discovery before quoting.
Can the agent run on our own infrastructure?
Yes. We deploy to your cloud account or your own servers, and use self-hosted models when data can't leave your environment.
How do you stop an AI agent from making mistakes?
Every agent ships with an evaluation set, tightly scoped tool permissions, confidence thresholds that route uncertain cases to a person, and step-by-step logs you can audit.
Which language model do you use?
Whichever fits the task, budget and data rules — commercial APIs or open-weight models. We design the system so the model can be swapped without a rebuild.
Can agents work with Salesforce, HubSpot or our internal tools?
Yes. We have built Salesforce and HubSpot integrations, and agents act through the same APIs and permissions a user would have.
Have something to build?
Tell us where things stand and what you need. A short message is enough to start.