1- EVERYBODY Will Have Personal Agents
- Personal AI agents will be as ubiquitous as smartphones
- Every individual — executives, students, everyone
- Agents that understand preferences, manage workflows, act on your behalf
2- Company Memory Becomes an Asset
- Organizational knowledge captured by AI = strategic asset
- Beyond databases — contextual understanding of how things get done
- Companies that curate “company memory” gain competitive advantage
3- Different LLM Form Factors
- Mobile — lightweight, on-device models for personal agents
- Desktop — mid-range models for professional work
- Server — full-scale models for enterprise & multi-agent orchestration
- Each optimized for latency, cost, and capability
4- LLM → Commodity / Agent/Harness → The Differentiator
- LLMs become interchangeable, cheap, widely available
- Value shifts to the agent harness layer
- Orchestration, memory, tool integration, planning = competitive moats
5- APPS → Tools/Skills, USERS → Agents
- Applications decompose into Tools & Skills agents can invoke
- Users represented by their Agents in digital interactions
- Design processes for AGENTS, not for users
- UI/UX gives way to API/Protocol design
6- Security/Governance Embedded in Harness
- Cannot be bolted on after the fact
- Permission models, audit trails, accountability
- Data access controls, compliance, rate limiting
- Identity verification for agent-to-agent interactions
- The harness IS the governance layer
7- Agent Discovery & Collaboration
- Discovering the right agent/capability becomes critical
- Agent-to-agent collaboration requires:
- Synchronized release systems
- Integration test subsystems
- Protocol standards for communication
8- Testing/Hardening/Benchmarking Becomes Mainstream
- Benchmarking — standardized agent evaluations
- Hardening — adversarial testing, edge cases, failure modes
- Regression testing — updates don’t break capabilities
- Red-teaming — deliberate attempts to make agents fail
- Agent QA becomes its own industry vertical