AI Agent Development: They Reason, Decide, and Execute
Chatbots answer questions. AI agents execute entire tasks autonomously: they research, compare, negotiate, and make decisions. A $7.5B market in 2025, projected to reach $183B by 2033. 2026 is the year of agentic AI.
Service Deliverables
What you get. No ambiguity.
Chatbot vs AI Agent: Not the Same Thing
The difference between answering and executing.
A chatbot answers predefined questions. An AI agent reasons toward a goal, breaks down tasks, uses external tools (APIs, databases, browsers), and executes actions autonomously. It can iterate, self-correct, and scale to multiple coordinated agents. It's the difference between an interactive FAQ and a digital employee.
Executive Summary
What you need to know to decide.
Autonomous AI agents are the natural evolution of generative AI. While chatbots answer, agents execute complete tasks: they qualify leads, process orders, monitor systems, and generate reports. 61% of CEOs are already integrating agents into core operations.
Typical investment: from EUR 5,000 for basic agents up to EUR 100,000+ for enterprise multi-agent systems. ROI in 6-12 months for high-volume repetitive processes. The primary risk is deploying without governance: we define autonomy limits and human-in-the-loop from the design phase.
Technical Summary for CTO
Architecture and implementation details.
Architectures based on ReAct (Reason + Act), hierarchical planning, and multi-agent orchestration. Frameworks: CrewAI for agent teams, LangGraph for state graphs, AutoGen for multi-agent conversations, and OpenAI Assistants API for native integration.
Infrastructure: FastAPI + Python as backend, vectorstores for long-term memory (Pinecone/Qdrant), observability with LangSmith/Helicone. Deployed in Docker containers with Kubernetes orchestration. Security guardrails and autonomy limits configurable by role.
Is It Right for You?
AI agents make sense for repetitive, complex, high-volume processes.
Who it's for
- Companies with high-volume repetitive processes that require reasoning (not just rules).
- Sales and support teams losing hours to manually qualifiable tasks.
- Organizations already using LLMs or chatbots that want to take the next step toward autonomy.
- Operations with multiple disconnected systems (CRM, ERP, email, databases).
- CTOs looking to automate multi-step workflows with human oversight.
Who it's not for
- Simple tasks solvable with business rules or traditional RPA.
- Organizations without APIs or programmatic access to their core systems.
- If you need a basic FAQ chatbot (we have LLM integration for that).
- Companies without budget for iteration: agents require continuous tuning.
- Processes where human error has irreversible consequences without possible oversight.
5 Types of Agents We Build
Each agent is designed for a specific domain.
Customer Support Agent
Resolves L1/L2 tickets autonomously. Queries knowledge base, CRM, and customer history. Escalates to a human when it detects frustration or out-of-scope complexity. Average 50% reduction in manual tickets.
Sales Qualification Agent
Qualifies inbound leads, researches companies (LinkedIn, web, CRM), drafts personalized proposals, and schedules meetings on the sales rep's calendar. Integrates with HubSpot, Salesforce, and Pipedrive.
Internal Operations Agent
Automates multi-step processes: processes orders, generates invoices, updates inventory, and sends notifications. Connects ERP, email, and management systems. Reduces manual errors and processing time by 65%.
Data Analysis Agent
Queries databases, generates dynamic SQL, interprets results, and produces executive reports automatically. Detects anomalies and proactively alerts. Connects with BigQuery, PostgreSQL, and data warehouses.
Content and Marketing Agent
Researches trends, generates briefings, writes SEO-optimized content, and adapts tone and style per channel (blog, social, email). Includes fact-checking and brand review before publishing.
Development Process
From idea to production-ready agent.
Discovery and Design
We map the process to automate, define the agent's objective, required tools, and autonomy limits. Deliverable: agentic design document with architecture and decision flow.
Prototype and Validation
We build a functional prototype with core tools. Testing with real business cases. Prompt tuning, guardrails, and reasoning logic adjustments. Iterative review with your team.
Development and Integration
Full agent development with all integrations (CRM, ERP, APIs). Exhaustive testing: edge cases, security, performance. Technical documentation and operations manual.
Deployment and Monitoring
Production deployment with gradual rollout. LLMOps monitoring: decision traceability, cost per execution, latency, and anomaly alerts. 30-day post-launch support included.
Risks and Mitigation
Transparency about what can go wrong.
The agent makes incorrect decisions
Human-in-the-loop configurable by risk level. High-impact actions (payments, shipments, external communications) always require human approval until trust is established.
Uncontrolled LLM API costs
Budgets per agent and per execution. Spend alerts. More efficient models for simple tasks (GPT-4o-mini, Claude Haiku) and premium models only where complex reasoning requires it.
Hallucinations in critical data
Agents connected to verified sources (your CRM, your database). Fact verification before external actions. RAG for grounding with your proprietary data.
Excessive dependency on a single LLM provider
Model-agnostic architecture: swap between OpenAI, Anthropic, Google, and open-source models without rewriting the agent.
Real-World AI Experience
We've been automating digital processes for 15+ years, and since 2023 we've been building production LLM solutions. We're not academic researchers: we build agents that deliver measurable ROI. LLM integration, RAG, and now autonomous agents for European companies with full GDPR compliance.
Frequently Asked Questions
What our clients ask before getting started.
What exactly is an AI agent and how does it differ from a chatbot?
A chatbot answers predefined questions or generates text with an LLM. An AI agent has a goal, reasons about how to achieve it, uses external tools (APIs, databases, email), executes actions, and self-corrects if something fails. It's the difference between an assistant that informs and a digital employee that executes.
How much does it cost to develop an AI agent?
It depends on complexity. Basic agents (single-agent, 2-3 tools): EUR 5,000-25,000. Agents with ML (classification, predictive analytics): EUR 25,000-80,000. Enterprise multi-agent systems (orchestration, multiple integrations): EUR 100,000-500,000+. Always with a detailed proposal before we begin.
How long does it take to have a working agent?
A basic functional agent: 4-6 weeks. A complex agent with multiple integrations and a multi-agent system: 8-16 weeks. We deliver a functional prototype by week 3-5 to validate the approach before building the full version.
What happens if the agent makes a mistake?
We design agents with human-in-the-loop: high-risk actions (payments, external communications, data modifications) require human approval. As the agent proves reliability, you can gradually expand its autonomy. Every decision is logged for audit.
Is it secure? What about our data?
Data is processed on European servers with GDPR compliance by design. Agents access only the data they need (principle of least privilege). Configurable guardrails: what it can do, what it cannot do, and what requires oversight. Full audit trail for every action.
Can the agent integrate with our CRM, ERP, or internal systems?
Yes, integrating with existing systems is a core capability. We connect with HubSpot, Salesforce, SAP, Odoo, PostgreSQL, REST/GraphQL APIs, and any system with a programmatic interface. If it doesn't have an API, we evaluate alternatives (controlled scraping, hybrid RPA).
Do we need proprietary data to train the agent?
You don't need to train a model from scratch. Agents use pre-trained LLMs (GPT-4o, Claude, Gemini) and access your data in real time via tools. What you do need is programmatic access to your systems and documented processes. If you use RAG, then yes, you need a knowledge base (documents, FAQs, manuals).
What if AI models or pricing change?
Our architecture is model-agnostic: you can switch between OpenAI, Anthropic, Google, or open-source models without rewriting the agent. We monitor cost per execution with budget alerts. If a provider raises prices, we migrate in hours, not weeks.
Which Process Would You Automate First?
Free agentic design session. We analyze your process, estimate savings, and architect the agent. No commitment.
Design my AI agent Technical
Initial Audit.
AI, security and performance. Diagnosis with phased proposal.
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