Fase 2 started. Brief for the MVP vertical slice (one synthetic project
end-to-end on MAF 1.9.0: debate -> blocking validator -> two-layer HITL +
provenance -> ExpeL learning) produced via /trekbrief from documented intent
(incremental-plan §Fase 2 + capability-map §1/§5 + Fase 1 findings), not live
Q&A per the project operating model. framing=preserve; phase_signals=high/opus.
Brief-reviewer gate passed 6/6 (all dimensions 5; verdict PROCEED_WITH_RISKS)
after 2 iterations: iter 1 flagged a no-op token-accounting criterion and a
false-positive on research-field order; 3 derivable refinements applied
(retargeted the UsageDetails assertion, added Open Questions for the concrete
local model + Foundry deployment names, added a topic-count note). Validator
green (0 errors/warnings).
3 research topics identified (gaps the capability-map did not resolve): native
HITL in MAF workflows, local_folder via MCPStdioTool + citation provenance, and
a real local-profile chat client (spikes used only FakeChatClient). Manual path:
stopped at the brief as a gated checkpoint; research must run before /trekplan.
.gitignore: ignore generated .html annotation + progress.json render-derivatives
under .claude/projects/ (the .md sources stay tracked).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH
Two independent grounded passes (installed-source introspection + official MS
Learn via MCP) produce a per-need adopt/keep decision table for using MAF
features well in Fase 2, instead of reinventing them.
Headline: Microsoft's Workflows "State Isolation" page documents verbatim the
exact footgun Spike B(b) found today — a reused Workflow accumulates agent
threads across runs; the fix is a fresh-instance-per-run factory. Our
fresh_workflow() IS the official pattern.
Key verdicts: ADOPT real UsageDetails token counts + a budget ChatMiddleware +
native builder round caps + GA @tool/MCP + observability; KEEP the hand-rolled
structural VerdictStore and inline validator (MAF memory/eval are the wrong
shape); ROLL a tiny role->deployment map (declarative is preview/not installed).
Corrections recorded: CLAUDE.md "Magentic experimental" stands at doc-level (no
code gate); Spike D extend_instructions is two-arg (source_id, instructions).
Skills answer: method-as-Skill yes (MAF consumes SKILL.md natively, experimental);
MAF-docs-mirror Skill no (rots vs live MCP); the digest lives in this map.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH
Independent post-hoc review (brief-conformance + code-correctness) of the
de-risk spikes against brief.md. Round 1 verdict was BLOCK (Spike B(b) fan-out
was a tautological counter); after remediation commit a2dff21 both reviewers
return 0 findings. Code-correctness reviewer verified the fan-out bleed fix
against MAF internals (AgentExecutor._session reuse + InMemoryHistoryProvider +
SessionContext.get_messages). Verdict: ALLOW. Fase 1 is clear to close.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH
/trekreview flagged the Spike B(b) fan-out experiment as BROKEN_SUCCESS_CRITERION
(BLOCKER): it asserted a per-client call_count reached 3 on a reused instance vs
1 on a fresh one — a tautology true for any un-reset mutable counter, independent
of MAF, that never exercised the real G2/B7 shared-Workflow state-corruption
footgun. It was a false-confirm of a de-risk assumption.
Rebuilt to observe genuine MAF thread state via the messages each participant
RECEIVES (new FakeChatClient.received_texts seam):
- shared_instance_conversation_bleed: a reused built ConcurrentBuilder Workflow
accumulates the conversation across .run() calls — run N's participants receive
runs 0..N-1's prompts/replies (measured [[p0],[p0,p1],[p0,p1,p2]], strictly
monotonic) => genuine cross-run contamination.
- fresh_instance_conversation_isolation: a fresh instance per run gives each a
clean thread => each participant sees only its own project ([[p0],[p1],[p2]]).
Assumption now CONFIRMED with a meaningful observable. findings-b.md gains a
Method note recording why it was rebuilt; README rows updated.
Also fixes the MINOR: a_groupchat.run_live now mkdirs the findings dir before
write_text so a post-disposal run does not lose the measured result.
Gate green: ruff check + format, mypy src, pytest 48 passed / 1 skipped.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH
Framework-neutral narrative of what portfolio-optimiser aims to achieve and the
two hypothesised approaches to the same method. Claude Agent SDK paragraphs
corrected by the user: the SDK spans both emergent (one agent + subagents) and
explicit orchestration (hand-written or agent-authored workflow script with a
non-LLM validator gate). The real difference vs MAF is ready-made named
constructs vs building blocks — not emergent vs explicit.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH
Verified comparison of Microsoft Agent Framework (ground-truth introspection of
installed agent-framework-core 1.9.0 + Microsoft Learn) and Claude Agent SDK
(Anthropic docs + npm/PyPI). Grounds decision D7: rebuild the same method on
Claude Agent SDK as a separate sibling repo, in sequence, sharing only the
spec + golden/conformance suite — not orchestration code.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH
Planning-only artifacts (no code yet). Plan A- after adversarial review
(critic REVISE -> revised; scope MIXED -> addressed; 19 findings, 0 overlap).
Ground truth: agent-framework-orchestrations is a separate GA 1.0.0 pkg
(-> dev dep); core is 1.9.0; MAF orchestrations are async. Next: /trekexecute.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01H9FyyENxebxVThjrn9et8C
Completes Fase 0 (skeleton & decision-lock):
- reference_domain.py + data/reference_projects.json: a synthetic
"anleggskostnad" portfolio (3 fictional construction-cost projects with
cost line items) as the framework's bundled reference input. Plain typed
loader (frozen dataclasses); the JSON-Schema data-source *contract* (B5)
is deliberately deferred to Fase 2.
- backends.py: Profile (azure|local) + ChatBackend Protocol seam +
AzureFoundryBackend/LocalBackend stubs + get_backend() selector
(fail-fast on unknown profile). Empty skeleton per D2 — create_chat_client
raises NotImplementedError until live wiring in Fase 1. Return type is the
MAF BaseChatClient (the common base of FoundryChatClient/OpenAIChatClient).
Quality gate green: ruff format + check, mypy (src) clean, 12 pytest passed.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01H9FyyENxebxVThjrn9et8C
The `agent-framework[all]` meta package pulls still-beta integrations
(azure-ai-search/cosmos/ollama/...) that force --prerelease and drag in an
ALPHA pydantic — unacceptable for the IR/validation layer (B1). Per the
official Semantic Kernel -> Agent Framework migration guide, install only
the packages we actually need:
- agent-framework-core / -foundry / -openai (all GA)
- pydantic pinned to stable 2.x (>=2.11,<3)
Resolves cleanly on the stable channel (pydantic 2.13.4, was 2.14.0a1).
Only remaining pre-release pin is Azure's own azure-ai-inference
(transitive via -foundry; no stable release exists yet). uv.lock committed
for reproducibility.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01H9FyyENxebxVThjrn9et8C
Privat MS-tenant tilgjengelig men kostnadstak: lokal profil default i
utvikling, Foundry/Azure kun målrettet/minimal, ingen tunge test-kjøringer.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01H9FyyENxebxVThjrn9et8C
