Agentic AI Development: Build Production AI Agents
Most AI agents impress in a demo and fail in production. Opsio builds agentic AI systems that survive real workloads — autonomous agents with governed tool use, retrieval, and human-in-the-loop controls, deployed and operated on your cloud with the reliability an enterprise actually needs.
Trusted by 100+ organisations across 6 countries
6-8 wk
Scoped POC to production pilot
Multi-cloud
AWS, Azure & Google Cloud
50+
AI & cloud engineers
ISO 27001
Certified delivery
What's Included
End-to-end agentic AI engineering — from the first scoping workshop to a monitored production system.
Agent Architecture & Tool Design
We design the agent's tool surface, permissions, and control flow — what it can call, what needs approval, and how it recovers from errors. Single-agent or multi-agent orchestration depending on the task, built for auditability rather than a flashy demo.
Claude Agent SDK & MCP Integration
Production agents on the Claude Agent SDK with Model Context Protocol servers connecting your data, APIs, and internal tools through a governed, reusable interface — so agents act on your systems without hard-coded, ungoverned access.
Retrieval & Grounding (RAG)
Retrieval pipelines that ground agents in your documents and data with the right chunking, embeddings, and re-ranking — so answers and actions are based on your reality, not the model's training data.
Evaluation & Guardrails
An evaluation harness that scores the agent on your real tasks before and after every change, plus guardrails and human-in-the-loop gates on irreversible actions. You ship on evidence, not vibes.
Deployment & Observability
Deployment on your cloud (AWS, Azure, GCP) with full tracing of every agent decision, tool call, and cost — dashboards and alerts so you can see what the agent did and what it spent.
Operate & Improve
We run the system post-launch: monitoring, evaluation on live traffic, prompt and tool iteration, model upgrades, and token-cost optimization — continuous improvement, not a hand-off.
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Head of IT · Löfbergs
From agent demo to a system you can run
Agentic AI is the step beyond a chatbot: instead of answering one prompt, an agent plans, calls tools, reads and writes data, and completes a multi-step task on its own. That autonomy is where the value is — and where most projects break. A single-turn assistant is easy; an agent that acts reliably against your systems, stays within guardrails, and recovers from failure is an engineering discipline. Opsio's agentic AI development practice exists to close that gap between a promising prototype and a system your business can depend on. We build on the models and frameworks that actually reach production. That means Claude and the Claude Agent SDK where autonomous coding and tool use matter, the Model Context Protocol (MCP) to connect agents to your data and services through a governed interface, and AWS Bedrock or Azure OpenAI where your cloud and compliance posture require it. We are model-pragmatic, not model-loyal — we pick the architecture that hits your accuracy, latency, and cost targets rather than forcing one vendor's stack onto every problem.
The hard parts of an agent are rarely the model call. They are the tool design and permissions that decide what an agent may do, the retrieval layer that grounds it in your data, the evaluation harness that tells you whether it actually works, the observability that shows you what it did, and the human-in-the-loop gates that keep irreversible actions safe. We engineer all of it, then hand you a system with tests, dashboards, and runbooks — not a notebook that works on one laptop.
And we don't disappear at launch. Agentic systems drift as models, data, and usage change, so we operate what we build: monitoring, evaluation on real traffic, prompt and tool iteration, and cost control. You get an agent that keeps working, and a team accountable for keeping it that way. Featured reading from our knowledge base: Machine Learning Cloud: Build, Deploy & Scale ML in Production. Related Opsio services: AI Development & Integration: Production AI, Shipped, IoT Solution Development — Sensor to Insight, Delivered, MLOps Services — From Notebook to Production, and Enterprise RAG Chatbots — Grounded in Your Data.
How Opsio Compares
| Capability | DIY / In-house | Generic AI Vendor | Opsio Agentic AI |
|---|---|---|---|
| Time to production pilot | 9-18 months | Fast but shallow | 6-8 weeks |
| Tool permissions & guardrails | Your team | Limited | Designed in |
| Evaluation harness | Rarely built | Vendor-defined | On your real tasks |
| Observability & audit trail | Your team | Basic | Full tracing |
| Model flexibility | Whatever you know | Locked to vendor | Claude / Bedrock / Azure |
| Operated after launch | Your team | Support only | Fully managed |
Ready to get started?
What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
Scoping & Feasibility
$10,000-$25,000
1-2 week engagement
Build to Production Pilot
$40,000-$120,000
Most popular
Managed Agent Operations
$5,000-$15,000/mo
Ongoing
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
Why enterprises choose Opsio to build agentic AI.
Production-first, not demo-first
We build for the failure modes that break agents in production — permissions, recovery, evaluation, cost — from day one.
Claude & MCP specialists
Deep expertise in the Claude Agent SDK and Model Context Protocol, backed by a live library of Claude Code and agentic engineering knowledge.
Model-pragmatic
We choose the model and framework that fit your accuracy, latency, and cost targets — Claude, Bedrock, Azure OpenAI — not a single vendor's stack.
Governed by design
Guardrails, human-in-the-loop gates, and full audit trails so legal, risk, and security can sign off.
We operate what we build
Monitoring, evaluation on real traffic, and cost control keep the agent working after launch.
ISO 27001 delivery
Secure delivery from 50+ AI and cloud engineers in Karlstad and our ISO 27001-certified Bangalore centre.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
A staged path from idea to a running, governed agent.
Scope & Feasibility
A workshop to pick the highest-value agentic use case, assess data and tool readiness, and define success criteria. Deliverable: a scoped design and feasibility report. Timeline: 1-2 weeks.
Prototype & Evaluate
Build a working agent against real tasks with an evaluation harness that measures accuracy, safety, and cost. Deliverable: an evaluated prototype with documented metrics. Timeline: 2-3 weeks.
Harden & Deploy
Add guardrails, human-in-the-loop gates, retrieval, tracing, and CI, then deploy on your cloud. Deliverable: a production pilot with dashboards and runbooks. Timeline: 2-3 weeks.
Operate & Improve
Run the agent with monitoring, evaluation on live traffic, iteration, and cost optimization. Timeline: ongoing.
Key Takeaways
- Agent Architecture & Tool Design
- Claude Agent SDK & MCP Integration
- Retrieval & Grounding (RAG)
- Evaluation & Guardrails
- Deployment & Observability
Industries Served by Opsio
Agentic AI applied where the workflow is genuinely multi-step.
Software & DevOps
Coding agents, automated code review, and CI/CD agents on the Claude Agent SDK — see our Claude Code consulting.
Financial Services
Governed agents for research, reconciliation, and reporting with audit trails and human sign-off.
Customer Operations
Agents that resolve multi-step service tasks end to end, not just answer FAQs.
Manufacturing & Supply Chain
Agents that read data, call systems, and act across planning and quality workflows.
Agentic AI Development: Build Production AI Agents — FAQ
What is agentic AI development?
Agentic AI development is the engineering of AI systems that act autonomously to complete multi-step tasks — planning, calling tools, retrieving data, and taking actions — rather than just answering a single prompt. In practice it involves designing the agent's tool surface and permissions, grounding it in your data via retrieval, building an evaluation harness to verify it works, adding guardrails and human-in-the-loop controls for safety, and deploying it with full observability. Opsio delivers all of this end to end and then operates the system in production.
How is an AI agent different from a chatbot?
A chatbot responds to one message at a time and typically only produces text. An AI agent pursues a goal: it decides what steps to take, calls tools and APIs, reads and writes data, checks its own progress, and completes a task with limited human input. That autonomy delivers far more value but requires real engineering around permissions, error recovery, evaluation, and governance — which is exactly what agentic AI development provides.
Which models and frameworks do you build on?
We are model-pragmatic. For autonomous coding and tool use we build on Claude and the Claude Agent SDK; we use the Model Context Protocol (MCP) to connect agents to your data and services through a governed interface; and we deploy on AWS Bedrock or Azure OpenAI where your cloud and compliance requirements call for it. We select the architecture that meets your accuracy, latency, and cost targets rather than committing to one vendor's stack.
How long does it take to build a production agent?
A typical path from scoping to a production pilot runs 6-8 weeks: 1-2 weeks of scoping and feasibility, 2-3 weeks to build and evaluate a prototype, and 2-3 weeks to harden, add guardrails, and deploy. Timelines depend on how many systems the agent must integrate with and how strict the governance requirements are. We front-load evaluation so you know whether the agent works before committing to full deployment.
How do you keep autonomous agents safe?
Safety is designed in, not bolted on. We scope each tool's permissions so an agent can only do what it should, gate irreversible or high-risk actions behind human approval, ground the agent in your data with retrieval to reduce hallucination, and run an evaluation harness that scores safety and accuracy on your real tasks before and after every change. Every agent decision and tool call is traced, giving you a full audit trail for legal, risk, and security review.
Do you operate the agent after launch, or just build it?
Both — and operating it is where agentic systems succeed or fail. Models, data, and usage patterns change, so an agent that worked at launch drifts without maintenance. Opsio offers managed operation: monitoring, evaluation on live traffic, prompt and tool iteration, model upgrades, and token-cost optimization. You get a system that keeps working and a team accountable for it, not a prototype left to decay.
What does agentic AI development cost?
A scoping and feasibility engagement runs $10,000-$25,000 and confirms the use case, design, and expected value. A build-to-production-pilot engagement typically ranges from $40,000 to $120,000 depending on integration and governance complexity. Ongoing managed operation is $5,000-$15,000 per month. Most clients start with the scoping engagement so the investment decision is evidence-based rather than speculative.
Can you work with our existing cloud and data?
Yes. We build on your existing AWS, Azure, or Google Cloud estate and connect agents to your data and systems through governed interfaces (MCP servers, APIs) rather than standing up a parallel stack. During scoping we assess your data readiness and integration surface so the agent works within your environment, security model, and compliance boundaries from the start.
Still have questions? Our team is ready to help.
Ready to build a production AI agent?
Most agents fail in production. Book a scoping call to design an agentic system your business can actually run.
Agentic AI Development: Build Production AI Agents
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