SofStack is an AI agent MVP development company that builds autonomous and semi-autonomous agents for enterprise workflows. Our agents read, decide and act across your business systems, with the controls your teams need to trust them.
An AI agent goes beyond answering questions. It completes tasks: updating records, routing requests, preparing documents and coordinating steps across tools. We build agent MVPs that prove this value on one high-impact workflow before you expand.
Types of AI Agents We Build
Agent type | How it works | Example |
Task agent | Completes one defined task using a set of tools | Creates and updates CRM records from emails |
Workflow agent | Runs a multi-step process with decision points | Processes an invoice from receipt to ERP posting |
Copilot agent | Suggests actions and waits for human approval | Drafts support replies and proposes refunds |
Multi-agent system | Specialised agents collaborate on a larger job | Research, drafting and review agents for reports |
Monitoring agent | Watches data or events and triggers actions | Flags delayed shipments and notifies customers |
Enterprise AI Agent Use Cases
Customer Support Agents
Agents that classify tickets, look up orders, resolve common requests and hand complex cases to human agents with full context.
Finance Operations Agents
Agents that match invoices, chase missing documents, reconcile transactions and prepare month-end summaries.
Sales Operations Agents
Agents that research accounts, enrich leads, update the CRM and draft follow-up emails for sales teams.
IT and HR Service Agents
Agents that handle access requests, onboarding tasks and policy questions across ServiceNow, Jira and HR systems.
Procurement and Supply Chain Agents
Agents that compare supplier quotes, track purchase orders and alert teams to delays or exceptions.
Agents Connected to Your Systems
Agents are only as useful as the tools they can use. We connect them to your existing platforms through APIs and secure connectors.
CRM: Salesforce, HubSpot, Microsoft Dynamics
Service and ticketing: ServiceNow, Zendesk, Jira, Freshdesk
ERP and finance: SAP, Oracle, NetSuite, QuickBooks, Xero
Collaboration: Microsoft 365, Google Workspace, Slack, Teams
Data: SQL databases, data warehouses and internal APIs
Standards: Model Context Protocol (MCP) and function calling for tool access
Guardrails That Make Agents Safe for Enterprise Use
Human-in-the-loop approvals for actions above a set value or risk level
Least-privilege permissions: each agent can only use the tools and data its task needs
Action logs: a full record of every decision, tool call and result
Limits and fallbacks: step limits, timeouts and safe handover to a human when the agent is unsure
Sandbox testing against realistic scenarios before agents touch live systems
How We Build an AI Agent MVP
1. Workflow mapping: we document the current process, decision rules, systems and exceptions.
2. Agent design: we define the agent's goal, tools, permissions and approval points.
3. Prototype in a sandbox: the agent runs on test data so your team can review its decisions.
4. Integration and testing: we connect live systems and test success rates, edge cases and failure handling.
5. Supervised launch: the agent starts with human approval on every action, then autonomy increases as results are proven.
How We Measure Agent Performance
Task completion rate without human correction
Time saved per task compared with the manual process
Error and escalation rate by task type
Cost per completed task, including model and infrastructure costs
AI Agent MVP Pricing
Engagement | Starting price | Timeline |
Workflow Discovery | From $2,500 | 2 weeks |
Agent Proof of Concept | From $5,000 | 3–4 weeks |
AI Agent MVP | From $18,000 | 8–12 weeks |
Dedicated Agent Team | From $6,000/month | Monthly |
Pricing depends on the number of systems integrated, workflow complexity and approval requirements.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An AI agent takes actions: it uses tools, updates systems and completes multi-step tasks toward a goal.
Which workflow should we automate first?
Start with a high-volume, rules-based workflow with clear success criteria and low risk per action, such as ticket triage or invoice matching. We help you choose during discovery.
Can agents work with our existing systems?
Yes. We connect agents to your CRM, ERP, ticketing and collaboration tools through their APIs or secure connectors.
How do you stop an agent from taking the wrong action?
Agents get limited permissions, approval steps for sensitive actions, step limits and full logging. They start supervised and gain autonomy only after results are proven.
Which frameworks do you use?
We use LangGraph, CrewAI, the OpenAI Agents SDK, the Claude Agent SDK and custom orchestration, chosen per project.
How long does an AI agent MVP take?
Most AI agent MVPs take 8 to 12 weeks. A proof of concept on one workflow can be ready in 3 to 4 weeks.
Automate Your First Workflow With an AI Agent
Tell us which process takes your team the most time. We will assess whether an agent can handle it and propose a starting plan.