step 1
Agent Discovery
We define the agent's scope: what task it performs, where it acts autonomously, where it waits for confirmation, which tools and APIs are needed, and which LLM model fits best.
Which of these situations sounds like you?
Manual work
Routine processes take up 30 to 50% of your team's time and don't require complex thinking, just attention and time
AI integration
You want to implement AI comprehensively and automate specific business processes
AI startup
You're building a startup where artificial intelligence is the core of the product and the main value for users
Competition
Your competitors are already using AI for automation, and market standards are shifting
Scaling
Your key processes, such as recruiting, sales, support, or analytics, require significant resources and are hard to scale
Agent functionality
You have a clear understanding of the actions and tasks the AI agent should perform, and just need to bring it to life as an MVP
Agent-First MVP is a product built around an AI core
The AI agent is the product's core. It takes a goal, breaks it down into tasks, selects the right tools, and completes the work on its own. A human sets the direction and reviews the outcome.
Agent-First MVP is a solution for startups building AI-native products, as well as for companies looking to automate processes, speed up task execution, or scale without expanding their team.
What's included in Agent-First MVP?
step 2
Prompt engineering and agent configuration
We design the system prompts: the agent's role, goal, constraints, and output format. We set up chain-of-thought reasoning to break down complex tasks and few-shot examples to guide correct behavior in specific cases.
step 3
Memory and knowledge base
Short-term memory retains context within the current session. Long-term memory is built on a RAG system using your documents, procedures, and client database. The agent understands your business and responds with your context in mind.
step 4
Tool integration
The agent gets access to the tools it needs for your tasks. Each one is connected as a separate module with clearly defined permissions: what the agent can read, write, and send.
Examples of typical tools: web search, email, calendar API, CRM, databases, external APIs, files, Slack, Telegram.
step 5
Human-in-the-loop and guardrails
We define three levels of autonomy: the agent handles low-risk actions on its own, medium-risk actions go into a confirmation queue, and critical actions are never taken without a human.
When confidence is low, the agent stops and asks for confirmation. Every action is logged in the audit trail.
step 6
Management interface and monitoring
A dashboard with current status, a confirmation queue, and task analytics. The team can see what the agent is doing, approve decisions before they're executed, and adjust its behavior as needed without a developer.
step 7
Testing, tuning, and deployment
We test edge cases and unusual inputs, and verify that the guardrails hold up. We compare prompt configurations on real tasks. We deploy to production with monitoring and alerts. We train your team to manage the agent and add new tasks.
AI agents for specific business tasks
AI Recruiter
Takes a job description, searches for candidates on LinkedIn, checks their fit, drafts an outreach message, and sends it. Tracks responses, schedules interviews, and hands candidates off to the HR manager.
AI SDR
AI Operations Manager
AI Researcher
AI Support L1/L2
Financial AI Assistant
Choose a format
Agent-First MVP architecture
Orchestrator + LLM
The agent receives a goal and independently plans the sequence of actions: what to do, in what order, and which tools to use. Under the hood are leading LLM models, selected based on the task, budget, and privacy requirements.
Memory
Short-term: the agent retains context within the current session. Long-term: a RAG system built on your knowledge base (product, clients, procedures, and so on). Personalization: the agent takes individual user preferences into account across sessions.
Tools
The agent works with real tools: web search, reading, writing to a database, sending emails and messages, calling external APIs, and processing files. Each tool is a separate module with clearly defined access rights.
Task assignment
A task can be assigned through chat, an API call, a scheduled trigger, or an event in an integrated system (CRM, task tracker, calendar, and so on). The agent accepts requests in free form or through a structured template, depending on how the team interacts with it.
Human-in-the-Loop
The agent operates autonomously within defined rules. A confidence threshold determines the point at which the agent stops and asks for confirmation. Safety guardrails are a list of actions the agent never takes without explicit permission.
Monitoring and control
A dashboard showing the agent's current status, a log of every action, and task analytics. You always have a full, real-time picture: what's been completed, what results were achieved, and where confirmation is needed.
What results will you get from Agent-First MVP?
A finished agent
in production
A fully functional AI agent, configured for your processes and ready to handle real business tasks.
Agent management
panel
A web interface with a dashboard, tasks, statuses, a confirmation queue, and analytics.
Specification and
documentation
A complete description of the architecture, prompt logic, control rules, and configuration instructions. A new developer can pick up the project without additional context.
100% ownership
of the codebase
A GitHub repository with the full code, with keys and access handed over to you once the work is complete.
Guardrails
and action log
A security system configured for your tasks. A complete log of every action the agent has taken throughout its operation.
Agent
development roadmap
We document the current state and define next steps: new tools, additional scenarios, expanded autonomy, and the transition to a multi-agent architecture.
What does this look like in practice?
case №1
AI SDR for B2B SaaS
Request: increase the number of qualified leads without growing the team.
Solution: the SDR agent takes an ICP, finds leads in LinkedIn Sales Navigator and open sources, researches each company, drafts personalized outreach, and sends it through Outreach. It tracks responses and passes warm leads to the CRM.
Result: within 8 weeks, lead volume grew by 340%, and the team now spends its time on qualified calls.
case №2
AI Researcher
Request: reduce time spent preparing reports. The team was spending 3 to 4 hours on data collection and analysis.
Solution: the agent takes a research request, finds relevant sources through the web and news aggregator APIs, analyzes the information, produces a structured report with insights, and sends it to the team in Slack.
Result: report preparation time dropped from 5 hours to 25 minutes.
case №3
AI support for SaaS
Request: reduce the load on a SaaS support team serving 2,000+ users. 4 specialists were handling 200 tickets.
Solution: the agent classifies incoming tickets, checks account status via API, finds an answer in the knowledge base, and responds to the customer. When confidence is low, it hands the ticket off to a human.
Result: 73% of tickets are resolved without human involvement.
Frequently Asked Questions
How is an AI agent different from a regular AI tool?
Most AI tools operate in a conversational mode. An AI agent works differently: you set a goal, and the agent independently carries out a series of actions in real systems, searching for data, sending emails, and updating records in your CRM. The difference lies in autonomous execution of real actions.
How much involvement is needed from our team?
Which LLM do you use?
Can the agent connect to our systems (CRM, Jira)?
Do we keep the code if we decide to develop the agent further on our own?
How long does it take for the agent to start delivering stable results?
Is Agent-First a good fit if we don't have a technical team?
Can the agent gain unauthorized access to sensitive data?
Have an idea? Let's bring it to life
We'll go over your project on a free 30-minute call and provide a preliminary estimate of cost and timeline.