--- type: "WebPage" title: "AI agents that get work done" description: "Context Studios builds AI agents for businesses – from research and document agents to multi-agent systems. The foundation is Hermes Agent, connected to your systems via MCP, with human approvals, full logging and tests before go-live. Start with a 4-week sprint, expand as a build – on your own hardware if you prefer." resource: "https://www.contextstudios.ai/ai-agents" language: "en" generated: by: "process:contextstudios-md/1" at: "2026-10-08T19:44:36.447Z" status: "stable" --- # AI agents that get work done Context Studios builds AI agents for businesses – from research and document agents to multi-agent systems. The foundation is Hermes Agent, connected to your systems via MCP, with human approvals, full logging and tests before go-live. Start with a 4-week sprint, expand as a build – on your own hardware if you prefer. ## What Sets AI Agents Apart from Chatbots? Understanding AI Agents 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. #### Multi-Agent Orchestration Multiple specialized agents coordinated by a supervisor. Ideal for complex tasks requiring different capabilities. - Complex Research - Content Pipelines - Data Analysis High #### RAG & Knowledge Agents Agents with access to your proprietary knowledge. Combines LLM capabilities with your enterprise database. - Customer Support - Internal Search - Document Analysis Medium #### Automation Workflows Task-specific agents with tool calling. Automate recurring processes with intelligent decision-making. - Process Automation - Data Processing - Reporting Low-Medium ## Security & control An agent acts inside your systems. That is why we build the boundaries first. #### Human approvals Critical steps such as payments, sending or deleting only run after approval. #### Least privilege Each agent only gets the tools and data its task requires. #### Guardrails Inputs and outputs are checked; budget and number of steps are capped. #### Traceability Every run is logged: which source, which tool, which decision. #### Tests before go-live Evals with your real cases – repeated after every model or prompt change. #### EU or your own hardware Run with EU providers or fully on-premise, without data leaving your company. #### GDPR We support your records of processing, data processing agreements and DPIA. #### Kill switch Agents can be paused at any time; a person can take over any task. ## Examples by industry What typical use cases look like. These are examples, not client figures. Multi-Agent RAG Agent Automation #### Crafts & Construction Automatic quote generation from blueprints and specifications #### Healthcare Documentation assistant: summarises care reports and prepares shift handovers – the decision stays with the professional. #### Hospitality Smart reservation management with automatic table optimization #### Retail Automated reordering based on sales trends and inventory levels #### Services Customer inquiry triage with automated appointment scheduling #### Automotive Fault diagnosis from OBD data with repair cost prediction #### Fitness & Wellness Personalized training plans based on progress tracking data #### Venture Capital Startup screening with automated due diligence preparation #### Incubators & Accelerators Portfolio monitoring with milestone tracking and investor reporting #### Marketing Agencies Campaign briefs to multi-channel content in brand voice #### Enterprise Company-wide compliance review of contracts and policies ## Frequently Asked Questions About AI Agents Everything you need to know about AI agent development #### 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. #### 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. #### 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. #### What's the difference between RAG and Fine-Tuning? RAG (Retrieval-Augmented Generation) supplements the model with external knowledge at runtime – ideal for dynamic data. Fine-tuning trains the model on specific data – better for style/format adjustments. For enterprise knowledge, we usually recommend RAG: cheaper, more flexible, and data stays current. #### 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. #### 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. #### 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. #### 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. #### How are AI agents monitored in production? We offer comprehensive monitoring options: Dashboards for agent activities, performance metrics, and decision logs. Automated alerts for unexpected behaviors enable fast responses. Admin interfaces allow pausing, adjusting, or manually overriding agents. The exact monitoring requirements are defined together based on your use case. #### What tools and systems can AI agents access? AI agents can communicate with virtually any system via the Model Context Protocol (MCP) and APIs: CRM systems (HubSpot, Salesforce), ERP software, databases (PostgreSQL, MongoDB), cloud services (AWS, Azure, GCP), email and calendar, document management, accounting software, and many more. Integration is done through secure, authenticated connections with granular permission controls. #### 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. Have more questions? Contact us ## Glossary: AI Agent Terms Key technical terms explained clearly #### Multi-Agent Orchestration The coordination of multiple specialized AI agents working together to solve complex tasks. A supervisor agent distributes tasks and aggregates results. #### RAG (Retrieval-Augmented Generation) A method to extend LLMs with external knowledge. Relevant documents are retrieved at runtime and provided to the model as context. #### MCP (Model Context Protocol) Open standard that lets AI agents access tools, data sources and applications. Versioned by date (currently 2026-07-28) and, since December 2025, part of the Agentic AI Foundation at the Linux Foundation. #### Tool Use / Function Calling The ability of an AI agent to use external APIs and tools. The model independently decides which tool is needed for a task and executes it in a structured manner. #### Vector Database A database optimized for embedding search. Stores text as numerical vectors and enables semantic similarity search. #### Embedding The numerical representation of text as a vector. Enables semantic comparison of texts based on meaning, not just word equality. #### Token Window Management The art of optimally utilizing an LLM's limited context. Includes token budget allocation, context compression, and selective retrieval. Often more important with large context windows than small ones. #### Context Engineering Designing optimal contexts for LLMs – from system prompts to tool descriptions to dynamic context composition. Replaces 'Prompt Engineering' as a more precise term for holistic AI system control. #### Agent Loop The cycle in which an AI agent operates: Observe → Think → Act → Verify. Repeats until the task is solved. #### Supervisor Pattern Architecture pattern where a central agent (supervisor) coordinates specialized worker agents and delegates tasks. #### Chain-of-Thought (CoT) Technique where the AI model reveals its reasoning process step by step, leading to better results on complex tasks. #### Guardrails Safety mechanisms that prevent AI agents from executing undesired actions. Examples: budget limits, approval requirements. #### Human-in-the-Loop (HITL) Process where critical decisions by an AI agent must be reviewed and approved by humans. #### Hallucination When an AI model generates plausible-sounding but factually incorrect information. RAG systems reduce this risk. #### Fine-Tuning Retraining an AI model on specific data to specialize it for certain tasks. Alternative to RAG. #### Agentic Coding AI-assisted software development in which agents write, test and revise code on their own. Examples: Claude Code, OpenAI Codex. #### Reasoning Models Models that work through several internal steps before answering. Now built into all major model families (Claude, GPT, Gemini), usually with adjustable reasoning effort. #### LangGraph Framework for orchestrating complex AI workflows with state management. Ideal for multi-agent systems. #### OpenAI Agents SDK OpenAI's official SDK for building AI agents. Features handoffs between agents, guardrails, and built-in tracing. #### Handoffs Mechanism by which AI agents transfer control to other specialized agents. Enables division of labor in multi-agent systems. #### Claude Agent SDK Anthropic's SDK for building your own agents on Claude – the same foundation as Claude Code (called Claude Code SDK until 2025). Offers tool integration, context management and MCP support. #### A2A (Agent2Agent) Open protocol that lets agents from different vendors hand tasks to each other. Version 1.0 since April 2026, maintained by the Linux Foundation. #### Agent Skills Open standard (agentskills.io) for reusable agent capabilities: a folder with a SKILL.md, instructions and scripts that many agent tools can read. #### Evals Automated tests for AI systems: a set of real cases with expected results that an agent is checked against before every release. ## Which task should an agent take on first? Tell us about a process that eats up time today. We will tell you honestly whether an agent fits and what a first sprint would look like. Discuss your use case Personal reply from the founder Free initial call No obligation ## What we build agents with As of 09/2026 We are not tied to any vendor. The foundation stays; models and tools get swapped when better ones arrive. #### 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 #### 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 #### 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 #### Knowledge & data RAG over your documents: usually Postgres with pgvector, a dedicated vector database for very large collections. - pgvector - Qdrant - Weaviate - Pinecone - Convex - Supabase #### 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 Open models on your own hardware: Local AI Agents As of September 2026. We keep this list current as the market moves.