AI Agent Development Services
Agents that take real actions in your systems, with the guardrails, evaluation, and human oversight to trust them in production.
Who this is for
The situations that bring people to us
Your team answers the same forty questions all day
Support, screening, and follow-up work follows patterns a well-built agent can handle, while your people handle the cases that actually need them.
Leads and follow-ups go cold in the queue
Speed is the whole game in outreach and follow-up. An agent that calls, messages, or triages within minutes changes outcomes that a next-morning human never could.
You tried a no-code agent. It embarrassed you once.
Off-the-shelf agents fail quietly and confidently. Production agents need constrained tools, logged decisions, and a human gate exactly where mistakes are expensive.
What we build
Concrete deliverables, not categories
Voice agents
Phone-native agents that hold real conversations, capture structured outcomes, and hand off to humans cleanly.
▸ Built in production: an outbound calling agent for university career-outcome follow-ups (n8n + Vapi + Twilio)
Workflow agents
Agents wired into your actual operations: CRM updates, document handling, triage, and multi-step processes orchestrated with n8n and LLM reasoning.
Product copilots
In-app assistants grounded in your data through retrieval, so they answer with your facts and act through your APIs, not generic chat bolted onto the sidebar.
How it works
The anatomy of an agent you can trust
An agent is not a chatbot with ambitions; it is a system. A trigger (an inbound call, a new lead, a schedule) starts the run; retrieval assembles the context the agent is allowed to know; the reasoning layer plans with a deliberately constrained toolset; actions execute through your real APIs; and a human gate sits precisely where a wrong action would be expensive.
The part most builds skip is the last node: every step logged, and an evaluation suite that replays real scenarios on every change. That is what lets you answer the only question that matters (is it still behaving?) with data instead of vibes.
Proof, not promises
Repo Fixer: an autonomous-dev agent, in the open
Our R&D autonomous-dev platform: an agent that reads a failing repository, plans a change, and opens a reviewable pull request, with a human on merge, by design.
How we work
Four steps, each with an artifact
Artifacts beat adjectives. Every step of an engagement ends in something you can hold us to.
Step 1
Scoping call
→ Written scope & estimate
Thirty minutes on what you are building and why. You leave with a written scope, an honest estimate, and our view on whether AI is even the right tool.
Step 2
Architecture sprint
→ System design document
We design the system before we bill for building it: data flows, model choices, failure modes, and the success measure we will be judged against.
Step 3
Build in weekly demos
→ Working software, week one
Short cycles, working software every week, and decisions made in the open. You see progress in the product, not in status reports.
Step 4
Launch & run
→ Monitoring, evals & handover
We ship it, instrument it, and either run it with you or hand it over with documentation your team can actually operate from.
Agent pilots are deliberately narrow: one workflow, one success measure, two to six weeks. You get a working agent on real traffic behind a human gate, plus the eval harness. Then we widen its authority only as the numbers earn it.
Stack for this work
Straight answers
Questions buyers actually ask
How much does it cost to build an AI agent?
A scoped production pilot (one workflow, real integrations, evaluation harness, human-in-the-loop gate) typically lands in the low-to-mid five figures (USD). The main cost drivers are the number of systems the agent must touch, the reliability bar, and how much conversation design the use case needs. Full multi-workflow deployments run higher.
How do you stop an agent from making things up or going rogue?
Four controls, layered: grounding through retrieval so it works from your facts; a constrained toolset so it can only take actions we defined; human approval gates where errors are costly; and evaluation suites that replay real scenarios on every change. Agents earn autonomy gradually; they do not start with it.
Can it integrate with our CRM, WhatsApp, Slack, or phone system?
Yes. Integrations are usually the point. We orchestrate with n8n and direct API work, which covers mainstream CRMs, messaging platforms, telephony through Twilio and Vapi, and internal systems through their APIs or webhooks. Unusual systems get a thin adapter.
How long does an agent project take?
A narrow pilot typically ships in two to six weeks, including the evaluation harness. Timelines stretch with the number of integrations and the strictness of the reliability requirement, not with the ambition of the prompt.
What is the difference between an AI agent and a chatbot?
A chatbot answers; an agent acts. Agents take multi-step actions in real systems (calling, updating records, moving workflows forward) which is why they need engineering that chatbots do not: constrained tools, action logs, evaluation, and human oversight at the expensive steps.
Related AI development services
Engineering deep-dives on ai agents
Next step
Talk to us about your ai agents project.
Thirty minutes. You leave with a written scope and an honest opinion, including whether this is the right tool at all.
Prefer async? hello@visionnexera.com · We reply within one business day.