What is AI advisory and why do organisations need it?

AI advisory helps organisations navigate the complex AI landscape to find real value beyond the hype. Most AI projects fail not because of technology, but because of context problems, coordination gaps, and capability issues. AI advisory addresses these practical blockers to help you build AI capabilities that actually work.

What is context engineering in AI?

Context engineering is the discipline of designing what AI knows, when it knows it, and how that knowledge is structured. It's about giving AI the right information at the right time. Most AI failures are context failures - the model is capable, but it doesn't have the information it needs to help you effectively.

What's the difference between AI tools and AI fluency?

AI tools are software you can access. AI fluency is the ability to think with AI - to know when and how to use it effectively, to structure problems for AI collaboration, and to evaluate AI outputs critically. Many teams have tool access but struggle to get consistent value because they lack genuine fluency.

How do skills-based AI systems differ from traditional AI agents?

Traditional multi-agent systems create separate AI agents for different tasks. Skills-based systems give one AI agent multiple skills - packaged expertise it can invoke as needed. Skills compound: every skill you create is available to everyone, forever. This approach is simpler to maintain and scales organisational intelligence.

Which AI model should my organisation use - Claude, GPT, or Gemini?

The best model depends on your specific use case, data sensitivity requirements, and integration needs. Claude excels at nuanced reasoning and safety. GPT has the largest ecosystem. Gemini offers strong multimodal capabilities and Google integration. We help organisations evaluate options against their actual requirements rather than following hype.

What is AI orchestration and how does it differ from simple AI chat?

AI orchestration coordinates multiple AI capabilities to complete complex tasks. Unlike simple chat where you ask one question and get one answer, orchestrated AI can break down problems, use different skills, access external tools, and maintain context across multi-step workflows.

What is the skills paradigm in AI systems?

The skills paradigm treats AI capabilities as packaged expertise that compounds. Instead of creating multiple separate AI agents, you give one AI agent multiple skills it can invoke as needed. Every skill you create becomes available to everyone in your organisation, forever.

What is MCP (Model Context Protocol) and why does it matter?

MCP is Anthropic's open standard for connecting AI to external tools and data sources. It standardises how AI models interact with services like databases, APIs, and file systems. MCP enables AI to take actions in the real world, not just generate text.

What are the main AI orchestration patterns?

The main orchestration patterns are: Chaining (sequential steps), Routing (directing to specialists), Parallelisation (concurrent execution), Orchestrator-Workers (manager coordinating specialists), and Evaluator-Optimizer (iterative improvement loops).

How do you build AI skills that compound over time?

Build skills that capture domain expertise, not just prompts. Document the reasoning and context, not just the instructions. Make skills modular so they can combine. Store skills in version control so they persist and improve. Every skill becomes organisational intelligence that grows with use.

PANDIONSTUDIO

AGENTS & ORCHESTRATION

AI Agents Explained

What they actually are, beyond the hype

In 30 Seconds

“AI agent” has become one of the most used and least understood terms in technology. People nod along in meetings, unsure what it actually means. Vendors use it to describe everything from simple chatbots to autonomous systems.

The core idea is simple: an AI system that can take actions, not just generate text. But the details matter enormously. Different communities use the same words to mean different things, and the architecture choices have real consequences.

This guide cuts through the confusion. Clear definitions. Key distinctions. Practical implications. No assumed knowledge.

What Is an AI Agent?

An AI system that can take actions, not just generate text.

A regular LLM (like ChatGPT or Claude in a browser) generates text responses. You ask a question, it answers. That's valuable, but it's fundamentally passive.

An AI agent does more. It can:

Use Tools

Search the web, execute code, query databases, call APIs, interact with software. The agent doesn't just suggest actions — it performs them.

Plan and Reason

Break down complex tasks into steps. Decide what to do next based on results. Handle multi-step work without human guidance at every stage.

Maintain State

Remember what's happened so far. Track progress toward goals. Build on previous steps rather than starting fresh each time.

Work Toward Goals

Not just responding to prompts, but pursuing objectives. “Book me a flight” triggers a sequence of searches, comparisons, and booking actions.

The key insight: Most “AI agents” in production today are actually agentic systems — LLMs with tool access and orchestration logic, operating with human oversight. Fully autonomous agents are still rare.

Agent vs Agentic: The Critical Distinction

This distinction has become crucial in enterprise adoption

AI Agent

•Single-task or specific function
•Rule-following with limited adaptation
•Executes assigned steps
•Example: Note summariser, booking assistant

Agentic AI

•Multi-step, complex goals
•Reasoning, planning, self-adjustment
•Orchestrates workflows, coordinates agents
•Example: Research system, code assistant

All agentic systems qualify as agents, but not vice versa. The term “agentic” emphasises agency — the capacity to plan, adapt, and coordinate.

Workflows vs Agents: Who Controls the Flow?

Anthropic makes a useful architectural distinction that clarifies most confusion:

Workflow

LLMs and tools orchestrated through predefined code paths. The sequence is determined by the developer. Predictable and consistent.

Example: Generate text → validate → translate → publish. Each step is fixed.

Agent

LLMs dynamically direct their own processes and tool usage. The model decides what to do next. Flexible but less predictable.

Example: “Research this topic” — the agent decides what to search, when to stop, what to synthesise.

The practical choice: Most organisations in 2025-26 are building workflows (predefined orchestration) rather than fully autonomous agents. This gives AI capability with human oversight and predictable behaviour.

The Skills Confusion

The word “skills” means completely different things in different contexts. This causes significant confusion when people use the same term for opposite architectures.

Anthropic Agent Skills (Official)

Filesystem-based resources that provide domain expertise. SKILL.md files with instructions, scripts, and reference materials. Claude auto-discovers and loads them when relevant.

Key characteristic: Deterministic structure, triggered loading. Published as an open standard in December 2025.

Vercel “Skills” (On-Demand Retrieval)

A retrieval system where the AI decides which documentation to fetch based on the task. Similar to RAG — the model guesses what knowledge might help and retrieves it.

Performance finding: 53% accuracy — no better than having no documentation. The retrieval decision introduced a failure point.

General Usage

Loosely refers to any capability: “This agent has coding skills,” “We need research skills.” Marketing language for features.

Key characteristic: Non-technical, describes outcomes not architecture.

The takeaway: When someone says “skills,” ask what they mean. Anthropic skills and Vercel skills are almost opposite architectures.

The Hierarchy: Agents, Knowledge, and Tools

How these concepts relate to each other

AGENTS

Systems that act — combine knowledge + tools + reasoning

↓

KNOWLEDGE

Guides decisions

Skills

Procedural “how to”

Digital Experts

Expert judgment

↓

TOOLS

Execute actions — search, code execution, API calls

Agents

Make decisions about what to do and when. Maintain state. Work toward goals.

Skills

Procedural knowledge that guides how to approach tasks. “When requesting supplier emissions data, use this template.”

Digital Experts

Captured expert judgment — what a specific expert would think. “What would a biodiversity specialist flag in this land use proposal?”

Tools

Deterministic capabilities. Same input, same output. Bash, file operations, web search, MCP servers.

Key distinction: Skills tell you how to do things. Digital Experts tell you what expert Y would recommend. They're complementary.

What Is MCP?

Model Context Protocol — the “USB-C for AI”

MCP is an open standard that enables AI agents to connect to external tools and data sources. Think of it as a universal plug: any MCP-compatible tool works with any MCP-compatible agent.

MCP Host

The AI application that uses external tools. Claude Code, VS Code extensions, custom applications.

MCP Client

Bridges communication between Host and Server. Handles the back-and-forth interactions.

MCP Server

The external service being accessed. Database, API, file system, web scraper.

Why MCP Matters

Before MCP, connecting an AI to external tools meant custom integration for each combination. Now, tool builders create one MCP server, and it works with any MCP host. Universal interoperability regardless of the underlying LLM.

Enterprise Reality Check

The hype says agents are transforming everything. The reality is more nuanced.

What's True

• Agents can handle complex, multi-step tasks
• They dramatically extend AI capability beyond chat
• Early adopters are seeing real productivity gains
• The technology has matured significantly in 2025

What's Overstated

• Only 6% of enterprises trust agents fully
• 40% of agent projects may be cancelled (Gartner)
• Governance and security challenges are real
• Most production systems need human oversight

The honest position: AI agents are genuinely powerful and getting better rapidly. But most organisations should start with agentic workflows(predefined orchestration with human oversight) before attempting fully autonomous agents.

Quick Reference: The Terminology Map

Same words, different meanings across communities

Term	Engineering	Business/Product
Agent	LLM + tools + orchestration	AI feature that does things
Agentic	Agent capabilities + oversight	Marketing term
Skills	Filesystem expertise (Anthropic) or retrieval (Vercel)	Features, capabilities
Digital Experts	Captured expert methodology, activated as context	“AI that thinks like [expert]”
Tools	Function calling, MCP servers	Integrations
Memory	Context persistence	“The AI remembers me”
RAG	Retrieval-augmented generation	“AI searches documents”
Workflow	Predefined orchestration paths	Business process
Agent Teams	Multiple agent instances coordinating via shared task list	“AI working in parallel”

When in doubt, ask for clarification. The same word often means different things to different people in the same meeting.

Questions to Ask (Yourself or Vendors)

About Architecture

• Is this a workflow or a fully autonomous agent?
• How does the system decide what context to use?
• What happens when it misses relevant information?
• How is state maintained across sessions?

About Governance

• What's the human oversight model?
• What guardrails are in place?
• How do you measure success?
• What can go wrong, and how is it handled?

Clarity Over Hype

AI agents represent a genuine capability shift — but understanding what they actually are (and aren't) matters more than adopting the latest terminology.

We help organisations cut through the confusion, understand the architecture choices, and build AI capability that actually works.

Start a Conversation ← Back to Agents & Orchestration

Where To Go Next

The Skills Paradigm

Deep dive into skills-based architecture: packaged expertise that compounds. How skills extend agents — and now agent teams.

Context Engineering

The discipline that powers agents. What AI knows, when it knows it, and how that knowledge is structured.

Passive Context Architecture

Why always-present context beats on-demand retrieval. The research behind 100% vs 53% accuracy.

Context Drift

The persistent challenge in AI memory. Why context degrades and what actually works for multi-session continuity.

Disclaimer: This content is for general educational and informational purposes only. The AI landscape evolves rapidly. Definitions and best practices referenced here reflect publicly available sources as of February 2026. For specific guidance, consult qualified professionals.