6 · Market Intelligence
AI architecture needs a technology radar inside the spec, because the vendor/model/tool landscape shifts constantly. This is knowledge, not architecture — but knowledge that drives architecture.
Landscape — an AI-native technology radar. Modeled on Thoughtworks' Tech Radar, with an extra blocked ring for hard constraints. Dated and scoped, so it expires on schedule.
landscape: Coding Harnesses
as_of: 2026-06-02
review_by: 2026-06-16
scope: Hermes
adopt:
- Codex
reason: best current cost/performance for Hermes backend use
trial:
- GLM
reason: cheapest credible coding-agent substitute
assess:
- Opus
reason: highest cognition, but expensive
caution:
- Qwen Coder
reason: weak MCP connection reliability in current tests
blocked:
- Claude Code
reason: current interpretation of Anthropic usage rules blocks backend harness
Because it is structured rather than prose, it becomes queryable: Why not Claude Code? What is the cheapest substitute? What needs retesting next week?
Capability matrix — the most-used view, made test-backed. Every matrix carries provenance (as_of, review_by, scope, evidence, method, confidence).
matrix: Coding Agent Candidates
as_of: 2026-06-02
scale: 1..10
higher_is_better: true
evidence: [bench#CODE-HARNESS-2026-06]
candidate coding mcp cost_efficiency cognition backend_allowed
Claude Code 9 10 5 9 no
Codex 9 7 8 8 yes
GLM 8 6 10 8 yes
Opus 9 9 4 10 yes
Qwen Coder 7 4 9 7 yes
Required metadata on every matrix: as_of: review_by: scope: evidence: method: confidence:
Tradeoffs — the shape of each option:
tradeoff: Claude Opus
+ strongest reasoning
+ strongest MCP
- expensive
tradeoff: GLM
+ cheapest
+ good coding
- China-centric
tradeoff: Codex
+ good coding
+ OpenAI ecosystem
- weaker MCP ecosystem
Recommendations — answer "what should I choose?":
recommendation:
if: prioritize = MCP -> choose: Claude Code
if: prioritize = Cost -> choose: GLM
if: prioritize = Harness -> choose: Codex