feat(confidence): add confidence discount propagation across multi-agent DAGs

[DebasmitaBose0]

• New module agentwatch/orchestration/discount.py — ConfidenceDiscountPropagator computes per-edge discounts from trust scores, hallucination risk, and confidence data, then propagates downstream via BFS topological traversal
• 7 tests in test_multiagent.py
• PR Body:

Summary

Implements MAG-010 — Confidence Discount Propagation Across Multi-Agent DAGs (#367).

When an upstream agent produces output with low confidence or high hallucination risk,
a discount factor is computed per DAG edge and propagated downstream. The cumulative
discount is applied to downstream confidence scores so untrusted inputs are reflected
in every dependent agent's evaluation.

Key changes

• agentwatch/orchestration/discount.py — new ConfidenceDiscountPropagator with:
  • compute_edge_discount() blends trust (50%), hallucination (25%), and confidence (25%) factors
  • propagate() — BFS traversal computing cumulative discount as 1 - ∏(1 - d_i)
  • apply_discount() — applies cumulative discount to a score
  • DiscountReport and DiscountEdge dataclasses for result introspection
• 7 tests covering high-trust (no discount), low-trust (discount applied), chain propagation, clamping, empty DAG

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Reviewing files that changed from the base of the PR and between 2bb8ed2 and 1baeaa6.

📒 Files selected for processing (2)

• agentwatch/orchestration/discount.py
• tests/test_multiagent.py

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• Create PR with unit tests

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🧪 PR Test Results

Check	Result
Tests (pytest tests/)	❌ failure
Lint (ruff check .)	❌ failure
Coverage (agentwatch)	73.41%

_{Python 3.12 · commit 1baeaa6}