In the evolving landscape of intelligent systems, the synergy between human capabilities and Agentic AI is beginning to reshape how decisions are made, value is created, and innovation is scaled. Rather than functioning in isolation, this partnership could leverage contextual reasoning, empathy and ethical judgement of humans alongside the autonomous actions and decision-making of Agentic AI.
Organizations across sectors are beginning to invest in Agentic AI systems that can operate with minimum human input. This pursuit of self-governing intelligence reflects a growing vision/ambition to optimize operations and reduce delays due to exploding cognitive load. However, positioning Agentic AI as the sole driver of intelligence overlooks the nuanced value of human insight and intent. The real breakthrough lies in harmonizing human effort with agentic AI capabilities.
While one segment of technologists invested in AI speaks of a rapidly-approaching era of artificial general intelligence (AGI), and sooner-than-later, artificial super intelligence (ASI), where humans may have little to no role in running worldly operations, the other segment of equally AI-oriented technologists believes that the real unfolding of intelligence may happen only with humans in the loop at all times, with varying degrees of control. We believe that the level of autonomy and agency that machines take onto themselves, with human approval, will determine how the next higher-order intelligence systems evolve.
The Evolving Phases of Human and Agentic Intelligence
To understand this evolving relationship, it’s important to first understand today’s landscape, where human intelligence drives majority of the decision-making across complex, creative, and ethical domains. However, with evolving advancements in Agentic AI, we are witnessing a steady expansion of machine capabilities that are getting integrated into everyday workflows and cognitive tasks. For instance, with Cursor AI [4], developers primarily focus on high-level problem-solving, design and error checking, while the AI Agent handles more standardized tasks like requirements documentation, code generation, test script creation, and some predictive tasks, such as suggestions around feature conflicts, code reviews and resolutions, and even integration with standardized development environments. With Open AI’s Operator [5], for instance, while a human user defines preferences, manages payment and oversees the process to align everything with their needs, the agent handles the pipeline of travel bookings via the web interface.
Phase 1: Foundational Stage of Agentic AI Adoption (6-12 Months)
Today, the interaction between human and AI agents is still nascent (hence the smaller intersection), with only a limited overlap in capabilities and collaboration. The need for agentic automation arises from the growing complexity and volume of information humans process, alongside the demand for speed and precision. However, the intersection remains minimal due to various limitations – on the one-hand, agentic intelligence is new and evolving, with both the technology and the ability of AI systems to replicate human-like agency, and on the other hand, humans’ reticence to use AI agents and their general mistrust in agentic autonomy persists. Other technological constraints, including fragmented datasets, shallow contextual understanding, and limited memory capabilities of agents, further reduce the potential for deep synergy.
Figure 1: Current state of symbiotic intelligence
Phase 2: Context-Aware Stage of Agentic AI Adoption (1-2 Years)
The synergy between human and agentic intelligence becomes increasingly apparent in the transitional phase, with more tasks being shared or co-managed between the two. To achieve this evolving partnership, organizations must prioritize technology integration, ensuring agents are embedded within business processes and have access to relevant, real-time data. Equally important is the emphasis on workforce readiness - through upskilling, mindset shifts, and collaborative design of human-agent interfaces that empower humans to decouple from existing operations and embrace higher-order skill development. As agents evolve with capabilities such as contextual memory, proactive reasoning, and adaptive behaviour, the human role transitions from continuous validation to supervision of semi-autonomous agentic operations.
Figure 2: Transitional stage of symbiotic intelligence (1-2 years)
Phase 3: Strategically Autonomous Stage of Agentic AI Adoption (2-4 Years)
In the advanced phase, agentic systems operate with high levels of autonomy and proactiveness, capable of making decisions, learning continuously, and interacting fluidly across domains (hence depicting the larger intersection). However, human oversight continues to be essential for ethical reasoning, nuanced judgments particularly in dynamically evolving situations with no past parallels on which agents could have been trained, and steering agents toward socially aligned outcomes. Strategic decision-making, creative pursuits, and sensitive contextualization are also important aspects of operations where human cognition still surpasses machine intelligence. Rather than replacing humans, this phase represents the peak of partnership where the synergistic overlap between individual spheres of autonomy and agency gets bigger and bigger — where autonomy and alignment create systems that are not just intelligent, but wise.
Figure 3: Futuristic state of symbiotic intelligence (2-4 years)
Navigating the Shift: Levels of Readiness Required
As enterprises accelerate the adoption of Agentic AI, understanding organizational readiness across key enablers becomes critical. Transitioning from traditional automation to intelligent, autonomous agents demands holistic readiness—spanning advanced technology frameworks, AI-fluent talent and cultural openness to co-creation. The framework below outlines the phased evolution of Human–Agentic AI collaboration, helping business leaders assess where they stand today, identify capability gaps, and visualize the path toward building scalable, responsible, and symbiotic intelligence systems across their value chain.
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Shifts
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Phase 1: Foundational
(6-12 months)
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Phase 2: Context-Aware
(1-2 years)
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Phase 3: Strategic Autonomy
(2-4 years)
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Tech Maturity
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- Based on text LLMs
- Rule-based execution
- Limited goal memory
- Limited real-time context awareness
- Single agentic systems, limited multi-agent orchestration
- Early capabilities in multimodal input handling
- Extensive human-in-the-loop reinforcement
- Patchy external integration (no agentic interaction protocols)
- Non-existent multi-party trust framework, hence agentic AI limited to in-house, and functional applications
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- Based on fully multimodal LLMs, large action models (LAMs) and large reasoning models (LRMs) with in-built agentic architecture
- Goal-based execution
- Significant or unlimited context injunction
- Multi-agent orchestration platforms
- Human-in-the-loop for strategic decision moments, pivots, or sudden context shifts
- Cross-functional and limited cross-industry applications emerge as cross-company trust frameworks get stronger
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- “World order reasoning-cum-action models” with neuro-symbolic or brain-mimicking interfaces
- Fully explainable, self-learning, self-correcting agentic systems
- Distributed agent architecture and computing systems design to manage the “Internet of Agents (IoA)”
- Strategic autonomy to agents with human-in-exceptions
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Data and Infrastructure Readiness
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- Heavily dependent on human-curated data pipelines
- Enterprises lack “data for AI” discipline
- Critical data storage issues
- Scalability challenges with short supply of AI data centres capable of cloud-native AI workloads
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- Hybrid data architecture combining structured and unstructured data sources into a single data architecture
- Self-cleaning semantic data layer
- Data centres available but traditional and AI-native workloads co-exist needing explicit integration
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- Agentic AI managed enterprise data systems
- AI-native data fabric with context aware knowledge graphs and real time data streaming pipelines
- Distributed data storage
- AI-native cloud architecture becomes mainstream
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Core Technologies Used in Agentic AI Systems
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- Implementation of reasoning and acting (ReAct) pattern, Chain of thought (COT) prompting, retrieval augmented generation (RAG) data pipelines, and vector databases.
- AgentGPT and Flowise frameworks for minimal interface design needs
- Early usage of LangChain and AutoGPT frameworks
Examples: Claude 3.7 Sonnet, Gemini 2.0 Pro, OpenAI Operator, Qwen 2.5 Max, DeepSeek R1, Manus AI as generalist agentic LLMs
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- Adoption of goal-based agentic frameworks, such as AutoGPT, or persona-based agents, such as MetaGPT
- Focus on building Agentic capabilities by utilizing AI frameworks, such as CrewAI, AutoGen, and AgentGPT
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- Universal agent interfaces
- Self-updating knowledge graphs
- Multi-modal foundation models
- Edge AI Agents for on device autonomy
- Secure, privacy preserving AI frameworks
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Potential Use Cases
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- Doctors using AI diagnostic tools (like IBM Watson Health) to detect early signs of cancer from scans.
- AI-generated storyboarding for films and series where AI agents dynamically generate unique storyboards, scripts, and dialogue options based on audience sentiment analysis and genre trends.
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- AI coordinates autonomous fleets with minimal human oversight for deliveries.
- AI agents co-developing business strategies by analysing market trends, simulations, and competitor insights.
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- Self-Optimizing Smart Factories where AI agents run entire production floors, adjusting machinery, schedules, and resources autonomously based on market demand, energy cost, and labour efficiency.
- Self-Healing Cyber Defences, AI agents autonomously detect attacks, patch vulnerabilities, and negotiate with attacker bots (in cyberwarfare situations) in milliseconds.
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State of Enterprise Adoption Readiness
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- AI fluency – limited basic AI fluency in non-tech users. Limited awareness of agentic AI systems
- AI expertise – significant scarcity of AI expertise in general, specifically in agent design
- Learning agility – Agentic AI users learn by mistakes, resulting in higher cost and rate of failure
- AI mindset – adoption resisted by human operators fearing job losses
- AI guardrails – nascent responsible AI guardrails, explicitly coded and limited to generalist bias avoidance or vertical context norms
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- AI fluency – basic AI literacy achieved, agentic AI usage grows but limited fluency with reinforcement feedback
- AI expertise – limited supply of complex systems designers. Process specialists struggle with intermediate level AI skills
- Learning agility – feedback loops improve but humans find agentic systems lacking in handling real-time nuances
- AI mindset – adoption improves as new job roles on the process side allay fears of displacements
- AI guardrails – early frameworks for responsible AI in agentic systems, but global protocols missing
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- AI fluency – higher-order AI usage capabilities amongst business users. Enterprises focus on interdisciplinary talent
- AI expertise – business and AI experts collaborate to innovate on AI-first process design
- Learning agility – agentic AI systems continuously learn alongside users, enabling continuous process reinvention
- AI mindset – continuous higher-order skilling becomes the norm as machine-human harmony creates new-to-world opportunities
- AI guardrails – world orders are established around ethical agentic systems as agentic AI drives most global supply chains
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Journey of an Agentic AI Business User
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- Ex: A non-tech employee tries to procure an Agentic CRM platform without fully understanding the foundational tech behind it, and the readiness the enterprise needs for such a system, resulting in n-go or suboptimal usage that hampers future business cases.
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- Ex: The same employee gains basic knowledge of Agentic AI and learns how to engage better with such platforms, thus seeking better platforms that fit with organizational needs.
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- Ex: The employee becomes agent-qualified, able to analyse data, evaluate platform performance, identify opportunities, and understand risks to strategically guide the organization’s horizontal multi-agentic adoption strategies.
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Table 1: Readiness Framework for Scaling Human–Agentic AI Synergy Across Adoption Phases
Existing examples of evolving Agentic Systems
- Cognizant- Neuro AI
Cognizant [1] came out with its Gen AI platform back in May 2023, but now it has integrated the platform with Multi Agent orchestration for decision making. Multi-agent orchestration enables enterprises to quickly discover opportunities, prototype solutions, and build AI decision-making use cases across entire business operations, improves company performance and drives new, tangible revenue streams, optimizing decision-making for real-world impact.
It offers different AI agents in one multi-agent system to offer various enterprise solutions
