Don’t Bet on Agents, Bet on What Agents Need
Rethinking Enterprise AI Agent Architecture
AI Agents are evolving at an extraordinary pace.
Today we have ChatGPT, Claude, Gemini, and Copilot. Tomorrow, new Agent platforms will emerge. For most enterprises, continuously chasing the most advanced Agent technology is neither practical nor sustainable.
A more resilient strategy is not to bind ourselves to a specific Agent platform, but to build the capability layers that all future Agents will need.
In other words:
Don’t bet on which Agent will win. Bet on what every future Agent will need.
The Three Layers of an Agent
Most discussions today focus on:
- Prompts
- Tools
However, a complete Agent architecture consists of at least three layers:
Skill = How the Agent Thinks
Tool = How the Agent Acts
Resource = What the Agent Knows
Skill
Skills define the Agent’s behavior, expertise, and reasoning patterns.
Examples include:
- Sales Assistant
- HR Assistant
- Project Assistant
- Finance Assistant
In essence, Skills belong to the Prompt Engineering layer.
Tool
Tools define the actions an Agent can perform.
Examples include:
- Send messages
- Create approval requests
- Query attendance records
- Create tasks
- Read documents
Tools belong to the Action layer.
Resource
Resources define the context available to the Agent.
Examples include:
- Current user
- Current organization
- Current department
- Current conversation
- Current chat members
- Current project
- Current knowledge base
- Current approval request
Resources belong to the Context layer.
Many Agents Don’t Lack Tools
Many teams continuously add more tools:
create_task()
create_approval()
send_message()
query_attendance()
Yet their Agents still fail to deliver intelligent experiences.
The reason is simple.
The Agent does not know:
Who is speaking
Where the conversation is happening
What the current discussion is about
Which project is being referenced
In many situations, the Agent does not need more hands.
It needs eyes and memory.
Lessons from ChatGPT
The evolution of ChatGPT over the past few years provides an important insight.
Many of its most valuable new capabilities are not Tools.
Examples include:
- File uploads
- GitHub repositories
- Google Drive integrations
- Projects
- Memory
These are fundamentally Resources.
Their value lies not in giving the Agent more actions, but in providing richer context.
The Next Stage May Be Context Engineering
Over the past few years, AI development has largely followed this path:
Prompt Engineering
↓
Tool Calling
↓
Context Engineering
Context Engineering is rapidly becoming the next frontier.
Future Agent performance may depend increasingly on the ability to:
- Acquire context
- Manage context
- Filter context
- Compress context
- Route context
Rather than simply calling more tools.
What Is the Real Enterprise Asset?
Many organizations believe their primary AI asset is the Agent itself.
In reality, Agents are increasingly replaceable.
The truly valuable and difficult-to-replicate assets are:
Organizational structures
Approval workflows
Business data
Knowledge bases
Chat history
Permission systems
Project relationships
Together, these form a company’s unique Context Network.
Foundation models will continue to improve.
However, no model inherently knows how a specific organization operates.
The true value of enterprise collaboration platforms lies in owning and managing these contextual resources.
Don’t Predict How Future Agents Will Orchestrate
Future Agents will inevitably face:
- Massive numbers of Tools
- Massive numbers of Resources
- Massive numbers of Skills
What seems almost certain is that they will not solve this problem by placing everything into a prompt.
Future orchestration mechanisms may include:
- Vector retrieval
- Knowledge graphs
- MCP registries
- Agent-to-Agent (A2A) communication
- Planning systems
- Routing agents
- Context compression
Or entirely new technologies that have not yet emerged.
Because of this uncertainty, enterprises should avoid betting on a specific orchestration mechanism.
A More Durable Strategy
For enterprise collaboration platforms, the goal should not be to build the smartest Agent.
The goal should be to become the capability platform that future Agents want to connect to.
Focus on building:
Skill Layer
Tool Layer
Resource Layer
And ensure they are:
- Standardized
- Discoverable
- Composable
- Secure
- Permission-aware
- Multi-tenant
- Observable
- Auditable
With this foundation, organizations can adapt quickly regardless of whether the future is powered by:
- ChatGPT
- Claude
- Gemini
- Copilot
- Microsoft Agent Framework
- Private enterprise Agents
- Future Agent platforms that do not yet exist
Conclusion
Organizations may not own the most powerful Agent.
But they can own the most complete, standardized, and orchestratable enterprise capability layer.
Do not bet on how future Agents will think.
Instead, ensure that when the next generation of powerful Agents arrives, they will want to connect to your Skills, Tools, and Resources.