01 • Product Direction
AI-first experience
Build interfaces that communicate confidence: clear next actions, instant feedback, and visible progress across every workflow.
02 • Engineering Direction
Capability-driven architecture
Model every action as a typed capability. Add policy checks, structured logs, and rollback paths for safer production automation.
03 • Team Direction
Momentum loops
Use weekly wins, monthly demos, and clear KPI dashboards to keep energy high and continuously motivate meaningful delivery.
MCP-Enabled Agent Skeleton
A clean starter pattern for robust orchestration: explicit capabilities, strict I/O contracts, and policy-aware handlers.
Code Example (Separated & Explained)
The code sample is shown in its own section so readers can scan the concept first, then inspect implementation details clearly.
const capabilities = [
{
name: 'searchDocs',
description: 'Search company knowledge base',
parameters: { query: 'string' }
},
{
name: 'createTicket',
description: 'Create support ticket',
parameters: { title: 'string', body: 'string' }
}
];
const mcpPrompt = `
You can call: searchDocs, createTicket.
Prefer searchDocs for evidence, createTicket on explicit request.
Return strict JSON payload for selected capability.
`;1) Capability list
Each operation is explicit, typed, and discoverable so both developers and models know valid actions.
2) Prompt policy
The prompt sets selection rules and output structure, reducing ambiguity and unsafe behavior.
3) Strict output
A strict JSON payload makes downstream execution predictable and easy to validate.
Production Patterns
- RAG + Verification: Ground every answer in trusted internal context and verify assumptions before action.
- DLQ + Guardrails: Route malformed outputs to dead-letter queues with auto triage and alerting.
- Streaming UX: Show partial response and phase-level progress for long-running operations.
- Observability: Add traces, metrics, prompt versions, and user feedback loops to close quality gaps.
Research Tracker
- Weekly signal scan: collect 3–5 meaningful updates.
- Score by impact: prioritize by relevance, maturity, and ROI.
- Monthly synthesis: turn learning into one focused implementation.
- Quarterly reset: remove noise and sharpen roadmap themes.
Related docs: README.md, research/README.md, research/trend-map-2026.md
2026 Trend Roadmap
Tier 1 (Now)
Reliability, evals/observability, multimodal production, retrieval quality, and safety engineering.
Tier 2 (Next)
Open-model optimization, coding agents, and enterprise agent platform standards.
Tier 3 (Explore)
Embodied systems, AI-for-science, and global regulatory/economic shifts.
Build something meaningful this week.
Pick one trend, one workflow, and one measurable outcome. Progress beats perfection.