Employ AI agents strategically without the chaos

Orchestrate workflows. Ensure security. Maintain control.

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Automate complex, multi-step workflows end-to-end
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Ensure your agents share an enterprise context
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Maintain full control over agentic AI implementation

Where intelligent agents can move the needle in your business

Generative AI answers questions. Agentic AI gets things done. By deploying AI agents that can plan, reason, and act autonomously across your business, organisations are moving beyond incremental productivity gains to genuine operational transformation — faster delivery, lower costs, and outcomes that scale.

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Customer service and support — AI agents handle routine queries, process requests, and resolve issues 24/7, reducing inbound volumes and freeing human teams for high-value tasks.
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Software development and testing — agents accelerate the full development lifecycle, from requirements through to deployment, reducing delivery time and costs.
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Finance and operations — invoice processing, approvals, reconciliations, and compliance reporting are automated end-to-end, reducing manual handling, errors, and processing time.
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Legacy modernisation — agents analyse and document legacy codebases at a speed and scale that human teams cannot match, reducing the time and risk of modernisation.
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Knowledge management and business analysis — agents synthesise large volumes of documentation and data to generate requirements, user stories, and insights in minutes.
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Compliance and risk monitoring — agents continuously assess workflows against policies and regulations, flagging anomalies and escalating issues before they become problems. 

Case Studies

AI-powered solution speeds up compliance checks by 270x

An IT services provider replaced manual and highly error-prone processes with an AI-powered compliance solution that makes compliance checks faster and more accurate.
7
weeks
to build the app
3 x
more accurate
compliance checks
Read the full case study
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Harnessing AI to rapidly deliver factory planning and support app

Global manufacturing company built an AI-powered app that facilitates human-robot collaboration to skills match team members for new assembly lines.
4
weeks
to deliver application
4
days
to ramp up
Read the full case study
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A small team transforms operations with AI-powered workflows

Taking an AI-powered platform approach, a leading insurer quickly added AI functionalities into its applications, including a generative AI-powered customer email triage system.
~ 2
weeks
to deliver project
~ 48,000
customer cases
processed in 4 months
Read the full case study
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Why agentic AI is harder to get right than most organisations expect

Deploying an AI agent is straightforward. Deploying one that performs reliably, safely, and at scale in a real enterprise environment is another challenge entirely. As organisations move from pilots to production, a consistent set of obstacles emerges — and understanding them is the first step to avoiding the most costly mistakes. 

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Governance is an afterthought, not a foundation — most organisations deploy agents first and design oversight later, leaving autonomy levels undefined, accountability unclear, and compliance at risk.
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Agents fail without enterprise context — an agent operating without a full understanding of your architecture, data, policies, and system dependencies quickly becomes unpredictable.
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Scaling is fundamentally different from piloting — the integration complexity, data quality requirements, and change management challenges that emerge at scale are of a different order entirely.
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Human oversight is harder to maintain than it looks — as agents handle more volume, the humans designated to supervise them often lack the time, training, or clarity to intervene when it matters. 
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The skills to orchestrate AI are scarce — Designing the systems, governance, and operating models that make agents trustworthy at scale requires different technical, strategic, and leadership capabilities.
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AI sprawl compounds risk over time — as agent deployments multiply across the business without a unified governance model, technical debt, security vulnerabilities, and conflicting autonomy levels accumulate.

Want to maintain control over your agentic AI implementation?

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Why a platform approach is the most sustainable path to enterprise agents

Most organisations begin their agentic AI journey with point solutions — a tool here, an agent there, each deployed to solve a specific problem. It works, until it doesn't. As agent deployments multiply, fragmentation sets in: disconnected systems, inconsistent governance, duplicated effort, and mounting technical debt. A platform approach changes the equation entirely — giving organisations the unified foundation they need to build, govern, and scale agentic AI with confidence.

Learn more about the OutSystems Agent Workbench
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OutSystems Agent Workbench provides the guardrails — quality controls and cost management capabilities turn promising prototypes into production-ready, trustworthy systems at speed. 
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Unified governance across every agent — centralised control over how agents are built, what they can access, and how they behave across your entire agentic portfolio.
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Enterprise context, built in — the OutSystems Enterprise Context Graph gives every agent a shared, real-time understanding of your architecture, data, and policy constraints.
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Avoid AI sprawl before it takes hold — a platform approach prevents the proliferation of disconnected agents across teams and business units, maintaining your architectural coherence and security posture.
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OutSystems integrates with leading agentic development tools — teams can employ Claude Code, OpenAI Codex, and Cursor within a governed environment, giving them freedom without sacrificing control.
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Lower total cost of ownership at scale — reusable components, shared context, centralised monitoring, and a single platform for apps and agents reduce duplication of effort and maintenance overhead.

Ready to turn agentic AI potential into real business outcomes?

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