Enterprises increasingly route tasks to the right model instead of relying on one model.
Market
AI, LLMs, Skills, Agents, and MCP Trends
A practical market dashboard covering leading LLM platforms, reusable agent skills, SKILL.md patterns, and Model Context Protocol adoption.
AI value is moving from model-only chat to governed access to tools and workflows.
Skills package repeatable instructions, examples, references, and optional code.
MCP is becoming a common pattern for connecting agents to tools and data.
LLM comparison
Which model is best?
| Platform | Best fit | Strengths | Watch-outs |
|---|---|---|---|
| OpenAI GPT family | General reasoning, coding, office workflows, agents | Strong generalist capability, tool use, coding, broad ecosystem | Governance, cost control, and data handling must be designed carefully |
| Anthropic Claude family | Long-form analysis, safety-sensitive workflows, agentic coding | Strong writing, reasoning, long-context work, safety focus | Tool permissions and MCP server security require strong controls |
| Google Gemini family | Multimodal workflows and Google ecosystem integration | Strong multimodal direction and productivity ecosystem fit | Best value depends on existing Google stack and use case |
| Open-source / local models | Private deployments, cost control, edge use cases | Control, customisation, offline/private options | Operational overhead, model evaluation, and security patching |
Capability dashboard
Practical comparison
Skills and agents
SKILL.md and reusable agent workflows
What is a Skill?
A Skill is a reusable workflow package that tells an AI assistant how to perform a specific task consistently.
- Repeatable instructions
- Examples and templates
- Optional scripts and references
What is SKILL.md?
SKILL.md is the playbook file. It describes the workflow, when to use it, and how the assistant should execute the task.
- Markdown-based instructions
- Clear trigger conditions
- Step-by-step operating model
Why it matters
Skills reduce inconsistent prompting and help teams standardise repeatable AI-driven work.
- Reusable knowledge
- Lower operational variance
- Better governance and quality
MCP trend
Model Context Protocol market direction
Phase 1 — Experiments
Developers connect assistants to local tools, files, APIs, and internal scripts.
Phase 2 — Enterprise integration
Teams start using MCP servers as standard connectors into tickets, cloud, security, and network systems.
Phase 3 — Governance
Security teams focus on identity, authorization, least privilege, monitoring, and supply-chain controls.
Phase 4 — Operational AI
MCP becomes part of a controlled agent architecture where AI can safely observe, recommend, and act.
Recommendation
Best enterprise approach
The best approach is not to select a single winner model. Use a multi-model strategy: select the right model for each workload, wrap it with governance, and connect it to enterprise systems through controlled tools, Skills, and secure MCP servers.