Now building Web, Blockchain & AI systems for founders worldwide
We build custom AI agents that work autonomously on your behalf — handling tasks, making decisions, and triggering actions without human intervention.
What this means
Chatbots answer questions. AI agents complete tasks. They can browse the web, call APIs, write and execute code, send emails, update databases, and chain dozens of steps together — all without a human approving each one.
The difference in value is enormous. A chatbot reduces one type of support ticket. An agent replaces an entire workflow that used to take a person hours.
We build production-grade agents — not demos, not proofs of concept, not wrappers around a ChatGPT API call. Agents with real tool integrations, reliable error handling, and observability you can trust.
An AI agent is software that can take a goal, decide the next step, use tools, and keep going until the job is done or a human needs to step in. It is a worker, not a Q&A box.
A chatbot answers questions and stops. An AI agent takes actions — calling APIs, updating systems, and chaining steps — until the task is complete. Longer explanation: AI agents for business.
Production agents start with the human workflow they replace, then a defined tool set, memory, and orchestration pattern. A prototype is tested against real data before a monitored production build ships. See how to build an AI agent in production.
Cost depends on the workflow, integrations, reliability requirements, and whether you need one agent or several working together. We do not publish a fixed price list. Request an Action Plan or contact us for a scoped estimate. Existing cost write-up: cost to build an AI agent.
This page is for autonomous agents that decide and act. Broader LLM features inside a product are covered on LLM Integration & AI Development. Voice-specific agents are covered on Voice Agent Development. Rule-based or AI-enhanced pipelines across tools are covered on AI Workflow Automation.
2–4w
typical single-agent build time
Tools
APIs, databases, CRMs, and third-party services the agent can act on
24/7
autonomous operation without human intervention
Problems we solve
The difference in value is a chatbot reducing one type of support ticket versus an agent replacing a workflow that used to take a person hours.
Chatbots respond to questions. An AI agent takes actions — browse the web, write code, send emails, query databases, call APIs, and chain multiple tasks together to complete a goal without a human approving each step.
Common work this service is built for: lead qualification, research and reporting, customer support automation, data extraction pipelines, internal workflow agents, and code review. If a human does it on a computer repeatedly, an agent can likely do it faster.
Tool-calling and API integrations connect the agent to your existing APIs, databases, CRMs, and third-party services — acting on real data in your real systems, not in isolation.
We build agents with retry logic, structured output validation, fallback paths, and observability tooling so you know exactly what the agent did, when, and why. Monitoring is built in from the start.
Services
From workflow analysis to production deployment. One team, end to end — no handoffs between a strategy team and a delivery team.
Built using LangChain, VoltAgent, and OpenAI — purpose-built for your specific workflow, not a generic off-the-shelf tool dressed up as custom.
Included on all projects
Systems that coordinate multiple specialised agents across complex workflows — each agent handling what it does best, the orchestrator keeping them aligned.
Included on all projects
Agents that connect to your existing APIs, databases, CRMs, and third-party services — acting on real data in your real systems, not in isolation.
Included on all projects
Monitoring, logging, error handling, and alerting built in. You always know what your agent did, what it decided, and when something needs attention.
Included on all projects
Structured prompt design, output validation, and evaluation frameworks so agent behaviour is reliable, measurable, and improvable over time.
Included on all projects
Agents improve with use. We build feedback loops and iteration cycles into every deployment so performance compounds rather than stalls.
Included on all projects
Use cases
These are example use cases for autonomous agents — not published client case studies. This site does not currently have a dedicated AI case study.
Qualify leads while you sleep
Research · Scoring · Outreach · CRM sync
Sales Automation — B2B SaaS
An autonomous agent that qualifies inbound leads by researching prospects, scoring fit against defined criteria, drafting personalised outreach, and logging everything to the CRM — without human involvement.
Example use caseReports that write themselves
Monitor · Aggregate · Summarise · Deliver
Intelligence Automation — Fintech
A multi-agent system that monitors market signals, aggregates data from multiple sources, writes structured reports, and delivers them to stakeholders on a defined schedule.
Example use caseOperations that run themselves
Extract · Reconcile · Route · Report
Operations Automation — Enterprise
An orchestrated agent system handling cross-department operational tasks — data extraction, reconciliation, internal ticket routing, and status reporting without manual handoffs.
Example use caseHow it works
Building reliable agents requires careful workflow analysis before a line of code is written. Rushing the design phase is how you get agents that hallucinate in production.
Everything in-house. Workflow analysis, architecture, development, evaluation, and deployment — all delivered by the Nextelligentia team. One team, kickoff to launch.
We map the exact human workflow the agent will replace — every decision point, tool used, and exception case. No assumptions.
We define the agent's tool set, memory strategy, and orchestration pattern. You review and approve before any code is written.
A working prototype against your real data and systems. We measure accuracy, latency, and edge case handling before committing to production.
Full implementation with error handling, retry logic, output validation, and observability tooling. Built to run unattended at scale.
We deploy, set up monitoring dashboards, and run an iteration cycle post-launch. Agents get better — we make sure yours does.
How we build it
Reliable agents need the workflow mapped, the tools defined, and validation in place before they run unattended.
We map the exact human workflow the agent will replace — every decision point, tool used, and exception case. Then we define the agent's tool set, memory strategy, and orchestration pattern. You review and approve before any code is written. Multi-agent systems coordinate specialised agents when the workflow needs it.
Integrations already listed for this work: REST APIs, PostgreSQL, MongoDB, Slack, HubSpot, and Gmail. LLM providers: OpenAI, Anthropic, Mistral, and Llama 3. Agent frameworks: LangChain, LangGraph, VoltAgent, and AutoGen.
Production builds include error handling, retry logic, structured output validation, fallback paths, and observability. You own 100% of the code, prompts, tool definitions, orchestration logic, and deployment configuration — no lock-in.
We deploy, set up monitoring dashboards, and run an iteration cycle after launch. Infrastructure already used: Python, Node.js, Docker, AWS, and Railway. Observability: LangSmith, Helicone, Datadog, and custom logging.
Fit check
We'd rather tell you now than waste 30 minutes on a call.
We're probably right for you if…
We're probably not the right fit if…
Tech stack
Agent Frameworks
LLM Providers
Infrastructure
Observability
Integrations
Common questions
Answered directly — including when the answer is that we're not the right fit.
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Tell us the workflow. We'll tell you whether an agent is the right solution, how long it will take, and what it will cost.