Products and Platforms
TCS MasterCraft™
TCS MasterCraft™: Enabling Cognitive and Intelligent Automation
Highlights
- The convergence of Agentic AI and Low-Code/No-Code platforms is enabling organisations to build intelligent, adaptive solutions faster than ever before.
- Combine Agentic AI and LCNC platforms to build intelligent, autonomous solutions in days—not months.
- Democratise app development by enabling business users to create AI-driven workflows without coding expertise.
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Tech convergence
Digital transformation is no longer a strategic choice but a business imperative. As organisations race to modernise their IT landscape, two powerful forces are converging to accelerate this journey; Agentic AI and low-code and no-code (LCNC) platforms. Together, they are reshaping how businesses build, deploy, and scale intelligent solutions with unprecedented speed and agility.
Agentic AI and LCNC
Low-code platforms are rapidly reshaping software development by enabling faster application delivery and empowering a broader range of users to build solutions. Simultaneously, Agentic AI is emerging as a game-changing layer, advancing beyond conventional automation to deliver intelligent, goal-oriented workflows that adapt dynamically to evolving needs.
Unlike traditional rule-based bots or predictive models, agentic AI systems possess the ability to take initiative, respond to evolving contexts, and work alongside humans to accomplish complex objectives. This collaboration fundamentally changes decision-making by shifting it from a purely human-driven process to a dynamic, symbiotic model. Humans provide strategic intent and ethical oversight, while AI contributes real-time insights, autonomous execution, and adaptive problem-solving.
Agentic AI’s impact
Agentic AI is revolutionising the way businesses tackle challenges and drive innovation, introducing a fundamentally new approach to problem-solving. Industries that rely on complex decision-making and dynamic environments will benefit the most from this shift. Sectors like healthcare, finance, and manufacturing can leverage autonomous AI agents for real-time diagnostics, risk assessment, and predictive maintenance:
- Autonomous workflows: AI agents can independently manage tasks such as lead qualification, customer support triage, or supply chain optimisation, reducing human intervention and improving efficiency.
- Hyper-personalisation: By understanding user intent and context, agentic AI can tailor experiences in real-time, enhancing engagement across marketing, sales, and service touchpoints.
- Scalable intelligence: When embedded into LCNC platforms, agentic AI empowers non-technical users to build intelligent apps—turning business analysts, marketers, and operations teams into citizen developers.
Adoption challenges
Despite the promise, integrating agentic AI with LCNC platforms presents several challenges:
- Trust and transparency: Autonomous agents must be explainable and auditable to gain stakeholder trust, especially in regulated industries.
- Data quality and governance: Agentic AI relies on high-quality, contextual data. Poor data hygiene can lead to flawed decisions and unintended consequences.
- Skill gaps: While LCNC platforms reduce technical barriers, designing effective AI agents still requires a blend of domain knowledge, UX thinking, and ethical foresight.
- Integration complexity: Seamlessly embedding AI agents into existing workflows and systems demands robust APIs, orchestration layers, and change management.
Overcoming these challenges requires a carefully planned, phased approach to implementation.
Integration steps
Organisations should consider the following roadmap to unlock the full potential of agentic AI and LCNC platforms:
- Start with high-impact use cases: Identify business areas where autonomy can drive measurable outcomes—e.g., marketing campaign optimisation, customer onboarding, or inventory management.
- Empower citizen developers: Train business users to leverage LCNC tools with embedded AI capabilities, fostering a culture of experimentation and ownership.
- Establish guardrails: Organisations can balance innovation with compliance by adopting a “responsible-by-design” approach—embedding regulatory and ethical considerations into every stage of development rather than treating them as afterthoughts.
Agentic workflows
Agentic workflows are AI-driven systems that operate like teams of digital specialists. Each “agent” is an AI entity trained to perform a specific role—analysing data, planning next steps, making decisions, or executing actions.
These agents work together in real time, exchanging context and continuously learning from one another to deliver business outcomes with minimal human intervention.
How it works:
1. Goal understanding: A user or system defines a goal—e.g., “Summarise market trends from recent customer data.”
2. Intelligent planning: A Planner Agent determines the steps needed—such as gathering data, analysing trends, and creating a visual report.
3. Collaborative execution: Specialised agents take over:
- The data agent fetches and cleans data.
- The insights agent finds patterns and opportunities.
- The report agent crafts a clear summary with visuals.
4. Continuous feedback and optimisation: The workflow self-reviews results, refining them before delivering a final output—accurate, fast, and ready for decision-making.
Way forward
Agentic AI and LCNC platforms are not just tools, they are enablers of a new operating model where autonomy, agility, and accessibility define success.
Forward-thinking enterprises will embrace this convergence to:
- Accelerate time-to-value by reducing development cycles from months to days.
- Unlock new business models powered by intelligent agents that learn and adapt.
- Empower every employee across the organisation with advanced tools and capabilities to drive innovation. Organisations can ensure this democratisation without creating chaos by defining role-based access, implementing standardised templates, and providing training to align creativity with business objectives.
In this new era, the winners will be those who can combine human creativity with machine autonomy, building solutions that are not only smart—but also scalable, ethical, and deeply aligned with customer needs.