AI on the PDT Project

Navigating the "AI Wild West" with structured approaches and realistic expectations for pipeline digital transformation.

The Reality of AI Experience

1

Initial Awe

Promises of perfection in 2 hours capture our imagination

2

The Rabbit Hole

4 hours later, emerging deflated and disheartened

3

Hard Truth

Jobs remain safe; AI cannot read minds or fill unclear requirements

AI: The Brilliant New Starter

No Common Sense

Understands everything but lacks practical judgement

Convincing Liar

Often doesn't know it's lying, making errors seem credible

No Self-Checking

Brilliant but doesn't verify its own work

Forgetful Genius

Forgets yesterday's work and over-complicates simple requests

These issues waste time, but structured approaches can unlock AI's true potential.

Building Effective AI Agents

Instead of relying on a single, generic AI model, leverage specialist agents—AI models fine-tuned or augmented with highly targeted knowledge bases for specific tasks or domains. This approach dramatically reduces "hallucinations" and increases accuracy by ensuring the AI operates within a defined, relevant information scope.

Product Spec Agent

Trained on Product Requirements Documents (PRDs), design specifications, and user stories. It can answer questions about feature requirements, scope, and dependencies.

Code Review Agent

Accesses your codebase, coding standards, and historical bug reports. It can identify potential issues, suggest optimizations, and ensure compliance with best practices.

Customer Insight Agent

Digests customer feedback, support tickets, and user survey data. It helps the team understand pain points, feature requests, and market sentiment.

Structured Agent Instructions

Role and Identity

Define the agent's specific role and expertise

Clear Objectives

Set measurable goals and success criteria

Contextual Background

Provide current state and pain points

Decision Framework

Establish priorities and escalation paths

Boundaries

Define limitations and compliance requirements

Why Structure Still Matters: Beyond Agent Instructions

Even with a meticulously configured agent – one with a clear role, defined objectives, and established boundaries (as discussed in the previous card) – the way we phrase our individual requests remains critical. A well-trained agent can still falter with vague instructions, delivering generic or off-target responses.

Vague Prompt Example

"Summarize this document for me."

Outcome: The agent might provide a lengthy, unformatted summary, miss key details, or focus on less relevant information, despite its overall capabilities.

Structured Prompt Example (SRS Applied)

"SITUATION: We need a summary of the attached Q3 financial report for our board meeting. REQUEST: Provide a 3-paragraph executive summary focusing on key revenue drivers and challenges. SUCCESS: The summary is concise, highlights financial performance, and is suitable for a non-technical audience."

Outcome: The agent delivers a highly targeted, actionable summary that meets specific needs, leveraging its deep understanding of its role and objectives.

This is where the SRS Method comes in. It provides a simple yet powerful framework for crafting clear, effective prompts that guide agents to deliver precise and valuable outputs, maximizing their potential even when they have detailed initial instructions.

1

SITUATION

Brief current state or change that's occurred

2

REQUEST

One specific action you want the agent to take

3

SUCCESS

How you'll know the request is complete

Breaking the Static Data Barrier with MCP

While Retrieval-Augmented Generation (RAG) significantly enhances AI by incorporating external knowledge, its reliance on static datasets limits its application in dynamic, real-world environments. To truly unlock AI's potential, seamless integration with live, evolving data sources is essential.

The Problem: Static Data Limitations

RAG excels at leveraging vast, pre-indexed knowledge bases. However, for applications requiring real-time insights or interaction with live systems, static data falls short. AI needs direct, dynamic access to operational data to remain relevant and effective.

The MCP Solution: A Standard for Dynamic Integration

The Model Context Protocol (MCP) provides a standardized, open-source framework for AI systems to dynamically integrate with any external data source or application. Think of it as the OPC (Open Platform Communications) of AI-system integration, ensuring interoperability across diverse platforms.

Compelling Example: Digital Twin Pipeline

Imagine a digital twin agent for a complex pipeline system. Linked via an MCP server, this agent gains complete, real-time knowledge of the pipeline's operational state, historical data, and engineering parameters, transforming it into a truly dynamic, actionable intelligence.

Real-time SCADA + Model Data Access

AI can query live operational data from SCADA systems combined with engineering models, providing instant insights.

Natural Language Queries

Users can ask the AI directly, "What's the temperature in temperature zone 3?" or "Summarize pressure in pressure segment 5 over the last hour," for intuitive data analysis.

Continuous Monitoring with Automated Alerts

AI continuously monitors data to detect anomalies in flow rates, vibration, or temperature, and issue threshold alerts automatically, enhancing proactive maintenance.

AI Applications for PDT Project

Scope Definition

Crawl existing documents to extract key features and create scored scope lists.

Vendor Proposal Vetting

Extract key points and score proposals against requirements automatically.

Test Data Generation

Generate and verify test data for vendor PoCs and system comparisons.

Meeting Transcription

Record and transcribe multilingual meetings for comprehensive documentation.

Industry AI Success Stories

92%

Line Pack Utilisation

Increased from 78% using AI-optimised compressor scheduling

87%

Faster Convergence

Reinforcement learning for flow optimisation

$12M

Cost Savings

Early valve failure detection 14-16 weeks in advance

30x

Performance Boost

Rust-based numerical solvers vs NumPy

Physics-Informed Neural Networks and hybrid architectures deliver measurable operational improvements.

AI as Force Multiplier

Structured approaches, specialist agents, and human oversight unlock AI's potential across all PDT project phases—from scope definition to operational optimisation.

Context is King

Clear requirements and structured prompts

Human in the Loop

AI augments, doesn't replace human judgement

Iterative Approach

Break complex problems into manageable chunks