Put AI agents to work

AI agents that get work done

Agents research, check documents and operate your systems – with approvals wherever a person has to decide. We build them on open standards, measurable and ready to hand over.

Discuss your use case See use cases

Start with a 4-week sprintMulti-agentRAGAutomationMCP & A2A

Understanding AI Agents

What Sets AI Agents Apart from Chatbots?

An AI agent is like a digital employee who independently completes tasks – not just answering, but acting, learning, and connecting with other systems.

Chatbot

Answers questions based on predefined rules or training.

FAQ Bots
Customer Service Chat
Order Status Queries

Reactive – waits for input

AI Agent

Acts autonomously: researches, decides, and executes actions.

Research Assistant
Data Analysis
Document Processing

Proactive – works independently

Multi-Agent System

Multiple specialized agents work together on complex tasks.

Content Pipelines
Due Diligence
Automated Reports

Orchestrated – like a team

Three kinds of agents

Which one fits depends on your task, not on the technology.

(01)

Multi-Agent Orchestration

Multiple specialized agents coordinated by a supervisor. Ideal for complex tasks requiring different capabilities.

High · Complex Research · Content Pipelines · Data Analysis

(02)

RAG & Knowledge Agents

Agents with access to your proprietary knowledge. Combines LLM capabilities with your enterprise database.

Medium · Customer Support · Internal Search · Document Analysis

(03)

Automation Workflows

Task-specific agents with tool calling. Automate recurring processes with intelligent decision-making.

Low-Medium · Process Automation · Data Processing · Reporting

Examples by industry

What typical use cases look like. These are examples, not client figures.

Automation

Crafts & Construction

Automatic quote generation from blueprints and specifications

RAG Agent

Healthcare

Documentation assistant: summarises care reports and prepares shift handovers – the decision stays with the professional.

Automation

Hospitality

Smart reservation management with automatic table optimization

RAG Agent

Retail

Automated reordering based on sales trends and inventory levels

Multi-Agent

Services

Customer inquiry triage with automated appointment scheduling

RAG Agent

Automotive

Fault diagnosis from OBD data with repair cost prediction

Automation

Fitness & Wellness

Personalized training plans based on progress tracking data

Multi-Agent

Venture Capital

Startup screening with automated due diligence preparation

Multi-Agent

Incubators & Accelerators

Portfolio monitoring with milestone tracking and investor reporting

Multi-Agent

Marketing Agencies

Campaign briefs to multi-channel content in brand voice

RAG Agent

Enterprise

Company-wide compliance review of contracts and policies

As of 09/2026

What we build agents with

We are not tied to any vendor. The foundation stays; models and tools get swapped when better ones arrive.

(01)

Agent harness

Our default is Hermes Agent (Nous Research, MIT licence): skills following the open Agent Skills standard, memory across sessions, a scheduler and isolated subagents. If your team already uses a framework, we build with it.

Hermes Agent
Claude Agent SDK
OpenAI Agents SDK
LangGraph
Google ADK
Microsoft Agent Framework
PydanticAI

(02)

Models

We choose per task: large models for planning and hard decisions, small and cheap ones for routine steps. Open models can run on your own hardware.

Claude
GPT
Gemini
Mistral
Qwen
DeepSeek
GLM
gpt-oss

Open models on your own hardware: Local AI Agents →

(03)

Protocols & standards

MCP connects agents to tools and data, A2A connects agents to each other. Both now sit with the Linux Foundation – no single vendor can withdraw them.

MCP
A2A
Agent Skills
AGENTS.md
OpenAPI

(04)

Knowledge & data

RAG over your documents: usually Postgres with pgvector, a dedicated vector database for very large collections.

pgvector
Qdrant
Weaviate
Pinecone
Convex
Supabase

(05)

Operations & quality

Every run is logged and traceable. Before go-live, evals check that the agent reliably solves the agreed cases – and again after every model change.

Langfuse
LangSmith
OpenTelemetry
Evals
n8n
Docker

As of September 2026. We keep this list current as the market moves.

Security & control

An agent acts inside your systems. That is why we build the boundaries first.

(01)

Human approvals

Critical steps such as payments, sending or deleting only run after approval.

(02)

Least privilege

Each agent only gets the tools and data its task requires.

(03)

Guardrails

Inputs and outputs are checked; budget and number of steps are capped.

(04)

Traceability

Every run is logged: which source, which tool, which decision.

(05)

Tests before go-live

Evals with your real cases – repeated after every model or prompt change.

(06)

EU or your own hardware

Run with EU providers or fully on-premise, without data leaving your company.

(07)

GDPR

We support your records of processing, data processing agreements and DPIA.

(08)

Kill switch

Agents can be paused at any time; a person can take over any task.

How we work on it

Workshop
Setup
Sprint
Build & Support

(01)

Sprint

Four weeks on one goal, with something that runs at the end.

4 weeks · fixed price after scoping

(02)

Build & Support

We build the project out and stay alongside you once it is live.

after scoping, ongoing · fixed price after scoping; support billed monthly

Included

(01)

Custom AI agent development

(02)

LLM integration (Claude, GPT, Gemini)

(03)

RAG systems & knowledge bases

(04)

Workflow automation

(05)

API integrations

(06)

Training & documentation

(07)

Ongoing optimization

Not included

(01)

Custom AI model training

(02)

Data annotation or labeling

(03)

Ongoing LLM API costs

How it runs

(01)

Discovery & Design

Use case analysis, AI capability assessment, architecture design

W1-2

(02)

Development

Agent development, LLM integration, knowledge base setup

W3-6

(03)

Testing & Optimization

Quality testing, prompt optimization, performance tuning

W7-8

(04)

Deployment & Training

Production deployment, team training, ongoing optimization

(01)

Everything you need to know about AI agent development

Frequently Asked Questions About AI Agents

(01)What is an AI Agent?

An AI agent is an autonomous software system that uses Large Language Models (LLMs) to independently execute tasks. Unlike simple chatbots, an agent can use tools, make decisions, and run multi-step workflows – without human intervention at every step.

(02)When should I use Multi-Agent instead of Single-Agent?

Multi-agent systems are suited for complex tasks requiring different capabilities. Example: A research agent researches, an analysis agent evaluates, a writer agent creates the report. For simpler, focused tasks, a single agent with multiple tools is often sufficient.

(03)How much does it cost to develop an AI agent?

It depends on the scope. We usually start with a sprint: four weeks on one clearly defined agent, at a fixed price after scoping – you get the proposal within 48 hours. If you want to sort your ideas first, start with a fixed-price workshop. Running costs come mainly from model usage; we estimate them with you up front.

(04)How long does implementation take?

A first production agent usually takes one four-week sprint. Larger projects – several agents, many connected systems, strict approval processes – we then expand step by step as a build, with a demo after each stage.

(05)Which technologies does Context Studios use?

Our foundation is Hermes Agent by Nous Research (MIT licence): skills following the open Agent Skills standard, memory across sessions, a scheduler and isolated subagents – runnable locally, in Docker or in the cloud. If your team already uses the Claude Agent SDK, the OpenAI Agents SDK, LangGraph, Google ADK or the Microsoft Agent Framework, we build with it. We pick models per task, for example Claude, GPT and Gemini, locally Qwen, DeepSeek or GLM. Tools connect via MCP, agents talk to each other via A2A.

(06)How is the security of AI agents ensured?

Critical steps only run after human approval, each agent gets only the minimum permissions it needs, and guardrails check inputs and outputs. Every run is logged; evals test the agent before go-live and after every model change. Standards are still emerging: NIST launched its AI Agent Standards Initiative in February 2026 – we follow the current state.

(07)How do you handle GDPR and data protection with AI agents?

Data protection is built into our architecture from the start (Privacy by Design). We use European hosting options, minimal data storage, and transparent processing protocols. Personal data is only processed when necessary for the task. We support the creation of Data Protection Impact Assessments and document all data flows for your compliance requirements.

(08)How widespread are AI agents in business?

Less than the hype suggests. According to Gartner (2026 CIO survey), only 17% of organisations have deployed AI agents, and more than 60% plan to within two years. At the same time, Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027 – mostly due to unclear value or high costs. That is why we start with a use case whose value can be measured.