Research Analyst Bot Upgrade Report

Date: 2026-03-02 (UTC)

The dominant failure mode in analyst bots is architectural mismatch, not raw model weakness. Pushing more agents into a workflow without matching coordination to task structure lowers quality while increasing cost.

What this run produced

Total added: 14 non-X items focused on architecture, evals, grounding, and benchmark design.

Strategic findings

Coordination-task fit over agent count. Multi-agent systems can outperform single-agent on parallelizable work, but degrade under poor coordination or sequential tasks. Orchestration should be a per-task policy decision, not a global default.

Eval harnesses are mandatory. The core unit is task outcome under multi-turn tool use, measured with trials, transcripts, outcomes, and mixed graders.

Citation reliability must be first-class. Analyst-grade quality requires report-level and claim-level citation checks, not post-hoc formatting.

Memory + grounding are infrastructure. Retrieval quality and dynamic memory organization are practical bottlenecks; stronger base models alone do not solve confident failure modes.

Opinionated architecture recommendation

Build a policy-driven orchestrator with selective parallelism: single-agent by default for low-entropy tasks, centralized orchestrator-worker escalation for decomposable multi-branch work, and strict avoidance of uncontrolled topologies in production unless benchmarks prove superiority.

Persist three mandatory artifacts per run: retrieval trace, claim-to-source evidence ledger, and synthesis decision transcript.

14-day implementation frame

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High-signal source anchors