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AI Agent Development Company

AI Agent Development Company,
From Prototype to Production.

We build production-grade autonomous AI agents, multi-agent orchestration systems, and RAG-powered knowledge assistants, not demos, not experiments. Real agentic workflows that ship to real users.

Start 3-Day Architecture Sprint See AI Projects
Senior AI Engineers Private Data Deployments 4 to 14 Week Delivery Eval & Monitoring Included
Single-Purpose Agents
Tool use · API calls · Decision logic
Multi-Agent Systems
CrewAI · LangGraph · AutoGen
RAG-Powered Agents
Vector DBs · Embeddings · Retrieval
Eval & Monitoring
LangSmith · Helicone · Arize
40+
AI Systems Shipped
4 to 14
Week Delivery
99%
Uptime on AI APIs
Capabilities

Every Type of AI Agent, Production-Ready

From single-purpose task agents to complex multi-agent orchestration, we build agentic systems that run reliably in production with observability, fallback logic, and human-in-the-loop oversight.

Autonomous Task Agents
Multi-step execution agents that browse the web, call APIs, write code, and take actions, with human-in-the-loop oversight for enterprise safety and auditability at every step.
Tool UseWeb BrowsingCode Execution
RAG-Powered Knowledge Agents
Agents that answer from your proprietary data, PDFs, databases, CRM records, wikis, with semantic search, citations, and zero hallucination guardrails built into every response.
PineconeWeaviatepgvector
Multi-Agent Orchestration
Complex workflows where specialist agents collaborate, a planner, researcher, writer, and reviewer, coordinated via LangGraph or CrewAI with structured state management and error recovery.
LangGraphCrewAIAutoGen
Customer Support Agents
Autonomous support agents that triage tickets, resolve common issues, escalate complex cases, and hand off to humans with full context, reducing support load by 60 to 80%.
Ticket TriageAuto-ResolveEscalation
Sales & Research Agents
Lead research agents, proposal generators, competitor monitors, and outbound personalization engines built for RevOps teams, turning manual research into automated intelligence workflows.
Lead ResearchProposalsOutbound AI
Custom LLM Fine-Tuning
Fine-tuned domain models (LoRA, QLoRA) for medical, legal, and finance applications where base models lack the accuracy and domain specificity your use case demands.
LoRAQLoRADomain Fine-Tune
Our Process

How We Build AI Agents for Production

Every agentic system we ship is engineered for reliability, with fallback strategies, human oversight hooks, cost controls, and full observability from day one. No cowboy AI deployments.

3-Day Architecture Sprint

Before writing a single line of code, we map your agent architecture, data sources, tool integrations, decision logic, and evaluation criteria. You get a full technical spec within 72 hours.

Eval-First Development

We define success metrics and evaluation benchmarks before building. Every agent is tested against your real data before production deployment, no guessing whether it works.

01
Use Case & Data Architecture
Map the exact task the agent must complete, the tools it needs, the data sources it queries, the guardrails required, and the success metrics to measure against.
02
Agent Architecture Design
Select the orchestration framework, model stack, memory strategy, tool set, and state management approach. Single agent vs. multi-agent decision made here.
03
Prototype & Evaluation
Build a working prototype, run RAGAS/LangSmith evaluations, measure latency and accuracy against benchmarks, iterate on prompts and tool logic before full production build.
04
Production Engineering
Build the full system with API layers, rate limiting, fallback logic, streaming responses, cost controls, and secure integration with your existing infrastructure.
05
Deploy, Monitor & Optimize
Deploy with full LLM observability (LangSmith, Helicone, Arize), token cost dashboards, error rate monitoring, and an optimization roadmap for continuous improvement post-launch.
Technology Stack

AI Agent Technologies We Work With

Orchestration
LangGraphCrewAIAutoGenLlamaIndexDSPy
Foundation Models
GPT-5.5Claude Opus 4.7Gemini 3.1 ProLlama 4Mistral
Vector Databases
PineconeWeaviateQdrantpgvectorChroma
Observability
LangSmithHeliconeArizeWeights&Biases
Start Your AI Agent Project

Book a Free Agent Architecture Audit

Tell us the task your agent needs to complete. A senior AI engineer will map the architecture, recommend the right framework, and give you a realistic delivery estimate, free, no obligation.

45-Minute Technical Call
With a senior AI engineer, not a sales rep
Agent Architecture Recommendation
Framework, model, data pipeline, and evaluation strategy
Realistic Delivery & Cost Estimate
Timeline, team size, and pricing before you commit
Related Services
Hire AI Engineers RAG Pipeline Development LLM Integration Services Hire Dedicated Developers
90-Day Warranty

Every AI agent we build ships with a 90-day warranty. If it breaks due to our code, we fix it at zero cost, no questions, no invoice.

Chat with our AI engineers
Talk to an AI Agent Engineer
// free architecture audit · no commitment
FAQ

Common Questions About AI Agent Development

Everything you need to know before your first architecture call. Still unsure? Talk to us

An AI agent can take actions, browsing the web, calling APIs, writing code, or sending emails, not just answer questions. Unlike a chatbot with scripted responses, agents use LLMs to reason through multi-step tasks, decide which tools to use, and execute autonomously with minimal human input.
A basic single-purpose AI agent with RAG and tool-calling takes 4 to 8 weeks. A production-grade multi-agent system with custom LLM fine-tuning, evaluation frameworks, and monitoring typically takes 8 to 16 weeks. Codioo's 3-day Architecture Sprint maps exact requirements before any code is written.
We build with LangGraph, CrewAI, AutoGen, and LlamaIndex for orchestration. Foundation models include GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, Llama 4, and Mistral. Vector databases include Pinecone, Weaviate, Qdrant, and pgvector. Stack selection is based on your latency, cost, and accuracy requirements.
Yes. We deploy on-premise AI agents using open-source models (Llama 4, Mistral, Gemma 4) via Ollama or vLLM on your private cloud or on-premise servers. Your data never leaves your environment, critical for healthcare, legal, and financial applications with strict compliance requirements.
Cost depends on scope: a basic single-purpose production agent involves far less engineering than a multi-agent system with custom fine-tuning, evaluation frameworks, and full monitoring. Codioo also offers a dedicated AI engineer retainer for ongoing agentic development and iteration. Share your requirements and we will scope it and quote a fixed price.
Ready to Build Your AI Agent?

Book a free architecture audit with a senior AI engineer. We'll map your agent requirements, recommend the right framework stack, and give you an honest delivery estimate, no sales pitch, no commitment.

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