78%
of organisations now use AI in at least one business function, up from 55% last year.
McKinsey, State of AI 2025
AI & Data
AI agents that handle the work, not just suggest it.
Most AI tools draft, summarise, or recommend. We build agents that plan, act, and deliver outcomes. They call your APIs, query your data, make decisions, and report back. You review exceptions. The rest runs itself.
From AI suggestions to AI that gets things done.
AI that suggests is useful. AI that acts is different. An agent receives a task, breaks it into steps, uses your tools and systems, checks the results, and delivers the outcome. A human steps in where it matters: approving exceptions, auditing decisions, changing direction. The rest runs on its own. That is a different class of system. It needs to plan, retry when something fails, stop when things look off, and explain why it made each decision. We build that with the architecture, testing, and safety work that holds up under real traffic. When a simpler AI feature is enough, we tell you.
By the numbers
AI adoption is now the default. Most pilots still fail in production.
Companies are using AI everywhere. The hard part is not the model. It is the engineering that turns a demo into something your business can rely on.
33%
of enterprise applications will include agentic AI by 2028, up from under 1% in 2024.
Gartner, 2024
1.5×
revenue growth for AI leaders versus peers, with 1.6× higher shareholder returns.
BCG, AI Maturity 2024
~80%
of GenAI projects fail to deliver production value, usually because orchestration, testing, and guardrails are missing.
RAND, 2025
What we build
The full stack of an autonomous agent system, designed as a product your team can extend.
Multi-agent architectures that fit the problem
Single agent or many, hierarchical or peer-to-peer, async or blocking. We design the layout around what your problem actually needs, not around a framework's default loop.
Runtimes that survive real traffic
Long-running agent loops, durable state, queues, retries, parallel execution, cost controls. The infrastructure that turns a working prototype into a system that handles production load.
Tool and system integrations
Agents that call your APIs, query your databases, post to your services, write code, and fetch from the web. With the authentication, error handling, and idempotency you expect from any production system.
Memory, planning, and reasoning
Working memory, long-term context, retrieval over past actions, planning loops, reflection, and self-correction. Each chosen for the problem, not for the framework.
Testing and safety guardrails
Behavioural tests, capability checks, resistance to manipulation, output filtering, and audit trails for every action. Agents need more than test datasets. We build the harnesses that catch drift before your users do.
A platform your team extends
Not a one-off demo. The runtime, test harness, prompt registry, and tool layer are built so the second agent costs a fraction of the first.
Selected work
Recent projects where autonomous agents replaced hours of manual work.
Insights
Web-based tool that simplifies and accelerates airline research and analysis activities. Additionally, its risk tracking capabilities simplify risk management, auditing and collaboration in order to find additional value for the airline.
View Case Study
Recast
Recast is a platform that resets what it means to stream. Making access fairer, rewarding creators better, and connecting through content. Where Fans pay creators per view, rather than the platform per subscription.
View Case StudyWhy Codino
- 10+ years shipping production systems for regulated industries. Not prompt-engineering demos.
- One team owns the full stack. Architecture, integrations, testing, guardrails, and runtime.
- Senior engineers based in the EU, with EU data residency and GDPR-compliant delivery by default.
- Testing-first. Every agent ships with test datasets, live monitoring, and a clear ROI baseline.
- Framework-agnostic. LangGraph, LlamaIndex, OpenAI Agents SDK, Anthropic tools. Chosen for fit, not for fashion.
- Phased rollouts. Humans stay in the loop until the data supports expanding autonomy. No big-bang launches.
How we build
From architecture decision to a deployed agent system. Each phase delivers a usable capability.
- 01
Design
System workshop. We map what the agent should decide, what it can call, what humans verify, and what state it holds. You leave with a system design, not a wishlist.
- 02
Prototype
A narrow agent loop, fully wired, on real data and real tools. You see the agent's behaviour on your problem. Usually within 3 to 4 weeks.
- 03
Harden
Test harness, safety guardrails, monitoring, cost controls, audit trails. By handoff the system runs unattended on a slice of real traffic.
- 04
Extend
Add capabilities, agents, tools. The platform grows from a single agent into a system your team keeps extending without rewriting the foundation.
What changes when you have an agent system
Book a workshopYou ship capabilities that were not possible before
Tasks that needed a human expert now ship as software. With audit trails, consistency, and scale that humans cannot match.
Your roadmap stops being limited by headcount
Need a second researcher, a third analyst, a fifth specialist? The agent scales with infrastructure, not with hiring.
Testing gives you a real safety case
Behaviour over time is observable, testable, and defensible. When a regulator, customer, or auditor asks how the system reached a decision, you can show them.
The system extends to the next problem
Runtime, tool layer, and test harness compound. Building agent #2 costs a fraction of agent #1.
Where agent systems make sense
Areas where putting autonomous agents at the centre delivers what simpler AI features cannot.
Autonomous research and analytics agents
- Multi-step research with search, synthesis, and citations
- Continuous monitoring agents with proactive alerts
- Domain-specific analytical pipelines
- Investigative processes with branching exploration
What Clients say about us
Pete Willcox
VP Product
"All members of the Codino team fit seamlessly into our delivery teams, building excellent relationships and always willing to go the extra mile to deliver on our Roadmap in a timely and efficient way. We have built extremely good relationships with them and they feel just like part of the team"
Recast
AI agents, explained
A typical LLM feature wraps a model around an existing workflow: text in, text out. An agent system puts an autonomous reasoning loop at the centre, with tools, memory, and the ability to plan and act. The system is built around the agent, not the other way round. Different architecture, different testing, different operational practices.
AI Process Automation replaces manual work that is already happening. The agent just does it faster and cheaper. AI agents build new capabilities that were not possible before: autonomous research, expert copilots, multi-agent decision systems, products built around AI. Different starting point, different success metric. Often clients run both in parallel.
A system workshop: what should the agent decide, what tools should it have, what state does it hold, what do humans verify. From there we build a narrow agent loop, fully wired, in 3 to 4 weeks on real data and real tools. You see the system's actual behaviour before scaling.
Behavioural tests, capability checks, resistance to manipulation, output filtering, and audit trails. We design for observability from day one. Every action is logged, every decision traceable, every escalation explainable. Autonomy expands gradually: low-autonomy modes first, expanded only when the test data supports it.
We pick what fits the use case. LangGraph, OpenAI Agents SDK, Anthropic tools and MCP, LlamaIndex, custom orchestrators. Models from OpenAI, Anthropic, Google, and open-source (Llama, Mistral, Qwen) depending on cost, latency, and data-residency constraints. Framework choice is an architecture decision, not a partnership one.
Let's Talk About Your Project
Get In Touch
Maciej Roman|CEO & Co-founder