THINK and ACT like Agentic AI: Learnings from the MCP Framework

Problem-solving is at the heart of every business decision, strategic initiative, and personal breakthrough. Yet, mosteven seasoned professionals — do not do it well, consistently and with desired impact, But we expect our digital agents to deliver solutions to our needs with perfection, always.  At least that is the endeavor behind designing the working of Agentic-AI. 

Machines are inherently dumb, and hence seek full intent clarity, modular decomposition, integration and interdependence protocols, data sources & linkages, optimization function and desired output format to deliver against the problem or request asked of them.  No gaps or skipping of steps allowed!  Hence machines do need to rely on framework, like MCP to gather requirements and deliver outcome in a structured stepwise process.  

I believe even humans will do much better problem solving if we employ MCP framework in our problem-solving approach in a systematic and disciplined manner. 

Multi Component Pipeline (MCP) framework, as the name suggests, breaks down a problem into a sequence of discrete, interlinked components, each responsible for a specific transformation, decision, or analysis or action, and collectively taking the process from problem to recommendation 

Let us do a quick tour through these components:

1. Problem Definition layer: Agent command requires a crisp articulation of the problem with boundaries, context, and measurable goals.  In fact, lots of effort is made to get the intent rightly converted into proper prompt.  Do we insist on same level of shared clarity of the problem, or rush into action basis vague understanding of the expectations?

 

2. Decomposition Engine: Seldom is the real-life complex problem solvable within one module or function or domain.  This component breaks the main problem into sub-problems or modules. It involves deliberate analysis of possible contributors to the problem and defining logical structure, dependencies, and execution order of components

 

3. Input Interface Layer and Preprocessing: Here the focus is to specify exactly what data is needed for each step, and how it should be transformed.  Agents then gather and validate required data, and transform raw input into structured, usable formats.  Given the access Agent has to infinite set of data points, it is paramount for Agent to clearly define the purpose and format for each data point.  Contrast this to our approach, where it is either data deluge, by gathering all available data, or data drought, wherein the assumption is of either data not there or incorrect 

 

4. Task Execution layer: These layers involve equivalent to module leaders or subcomponent leads executing the specific sub-tasks, such as analysis, classification, or routing and creating intermediate outputs.  The clearer the task is the more efficient the delivery.

 

5. Reasoning / Aggregation Layer and Explanation Generator: This component integrates intermediate outputs from task modules and applies rules, heuristics, or logic to compose overall coherent conclusions.  Insights may come from different fields including heuristics, quantitative analysis, best practices, empirical evidence, scientific research and this layer collates outputs.  Further explanation generator produces interpretable outputs, with rationales and confidence levels against each set of conclusions.  

 

6. Recommendation Engine: MCP clearly differentiates the reasoning layer and the recommendation layer, helping creation of multiple recommendations against conclusions.  This component maps conclusions to each actionable outputs, such as decisions, next steps, alerts, or strategy recommendations, and present recommendations with priority or ranking.  As user, if you are looking for accuracy or for variety, you can indicate Temperature level accordingly during the prompt itself.  In our problem-solving approach, we too would gain to ascertain the level of accuracy or creativity expected from the answer, and keep discipline in separating the conclusion and 

 

7. Output Delivery Layer: This component formats and sends final output to the designated system or user, such as a UI, dashboard, workflow tool, or report.  A bit of thinking on how user would prefer to consume the outcome, will greatly enhance the impact and reduce rework.  Choices of charts, words, depth of data, sequencing of arguments, and tabulating of recommendations greatly influence the quality of the recommendations as viewed at the end.

 

8. Governance & Audit Layer: This component logs inputs, processes, versions, and decisions for traceability, compliance, and accountability. It often includes versioning and role assignments.  The discipline of maintaining evidence of decision inputs, decision contributors and influencing assumptions in documented traceable form is essential requirement, often forgotten unless specifically traced to closure.

 

9. Decision Feedback & Learning Loop: To me this component is the most powerful part of the MCP framework.  Based on the results, one can trace and identify components that needs strengthening by updating parameters, models, or resources linked to that component.  The evolution of problem-solving capabilities over time is embedded in the framework itself.  

 

Most of our daily life problem solving approaches fall into avoidable traps: poorly defined problem statements, inadequate or noisy data, uncoordinated module outcomes, jumping to conclusions, or delivering recommendations in unconsumable formats. 

MCP is truly a meta-cognitive scaffold and its adoption as problem solving framework would enhance clarity, objectivity, rigor, explainability, traceability, and acceptance to human endeavors, as much as it does for Agents.

Machine Human collaboration path starts with defining guidelines

Humans are in the driving seat, so get to decide the rules of the collaboration setting. However, as the arrangement evolves, machines may also start taking autonomous execution and restricts flexibility.  

   

Overarching principles and design guidelines 

1. At all times, HR has to be aligned with business goals and organisational stated values.  Talent is a key strategic asset, and its acquisition, growth, deployment for business goals is the core non-negotiable accountability.
2. Building a positive work environment, with shred sense of equity and fairness to maximize productivity and satisfaction remains core.  
3. Accountability towards Legal, ethical, and policy compliance across geographies, especially in multi-sector environments, is non-negotiable.
4. All decisions recommended by Machines (AI) should be explainable and may require human review — especially in critical or ethical contexts. 
5. There should be Intelligence-Led Decisioning ie decisions to be informed by predictive insights, prescriptive analytics, and scenario modeling provided by machines.
6. Agent-Led Execution of the repetitive and logic-based tasks. There is openness to redefine the present processes, workflows and decision rights to make tasks amenable to agent led delivery.  
7. Continually strive towards hyper-personalisation, based on profile, experience, interest, intent, and opportunity capture
8. High standards of Digital Ethics and Transparency supported by Clearly define data boundaries, model transparency, and ethical standards for use of employee data and AI-led decisions
9. Modular & Platform-Based Architecture, allowing for easy integrations with other systems and introduce agents. 
10. Reimagination and experimentation approach supported by feedback loops to continue evolving and moving on the evolution trajectory
11. Pace and realm of transition to accommodate employee's adaption challenges, keeping intact trust while managing negative associations like privacy concerns, algorithimic bias or over de-humanisation. 
12. There is always human escalation path available for any automated processes.      

Translating these principles and guidelines into action would require setting up new institutional structures like joint ethics and governance committees and cross functional teams, enhanced role of CHRO, CDOs and other leaders.  

Besides, this would require reimagining of the role of HR workforce as they share their work with machines going forward.       

Handing over to Machines : The Why and why-not Question?

Like all dimensions of work, the conventional work done by Human resource professional is also under scanner and expected to be given to, fully or partially, to machines.  

The prime motivation is to leverage the tireless energy, unbiased application of decision rules, immense retrieval and computing capabilities and NLP based interactions of new set of digital technologies (RPA, AI ML, Chatbot, Agentic-AI) for the good of employee, managers and organisation.  

The Why-Machine question?

The key motives for introducing machine power in HR context hovers around the following:

1 If Talent is the strategic asset, then machines help identify, acquire and deploy this asset better, and more efficiently. It can provide fitment score. predict the performance outcome, attrition risk and often provide the hidden talent option that is hidden in the remote work location.    

2 If employee experience at workplace is to be as good and personalised as at market-place, we can emulate same solutions for validation, interaction, recommendations and transactions, chatbots for example.    

3 If trust, transparency and auditability behind routine transactions are important, machines deliver better and at scale, using RPA as a disciplined agent.   

4 If machine releases cognitive capacity of the workforce, by taking over routine and rule-based decisions, why not? And machine capacity to adjust to scale is far more than of workforce. 

5  If the outfall of mistake by machine is manageable and not disproportionate from ethical, reputational and economics consideration, lets go ahead.   

In recognition of the above, over 70% big corporations are using machines in HR for some purpose or other.  

The Why-Human question?

At the same-time, need for putting Human in the loop is often felt: 

1 If the decision or action would create negative consequence, (say disciplinary action, dismissal), human judgement is warranted?

2 If there is less confidence in defending explainability of decisions or actions by machines to externl party, keep Human in the loop

3 If the empathy and emotions are as important as the content of the interaction for employee satisfaction, oblige

4 If the reliability of the data sets driving transcations and decisions are a suspect, let humans decide

5 If the decisions require case-2-case considerations with high level of contextual and subjective discretion, humans are not replaceable.

Moving work from humans to machines has to be a deliberative, and structured transition, ensuring coordinated readiness around policies, processes, technology and people dimensions, to make it seamless and well recived by stakeholders.