We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a complex query, our system decomposes it into a directed acyclic graph (DAG) of sub-questions, executes them through domain-specific agents in parallel, verifies result completeness via LLM-based evaluation, and adaptively replans to address gaps. The key contributions are: (1) dependency-aware parallel execution over a DAG of sub-questions with automatic context propagation, (2) verification-driven adaptive replanning that uses an LLM-based verifier as an orchestration-level coordination signal, and (3) configurable stop conditions that balance answer quality against resource usage. On 25 expert-curated market research queries, VMAO improves answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (1-5 scale) compared to a single-agent baseline, demonstrating that orchestration-level verification is an effective mechanism for multi-agent quality assurance.
Figures & tables
Figure 1: (a) VMAO framework architecture showing the iterative Plan-Execute-Verify-Replan loop. (b) Agent taxonomy organized by functional tier with information flow from data gathering through analysis to output generation.
Field
Description
id
Unique identifier (e.g., sq_001)
question
Specific, answerable question text
agent_type
Agent from taxonomy to handle this question
dependencies
IDs of sub-questions that must complete first
priority
Execution priority (1–10); higher = more important
context_from_deps
Whether to include dependency results in prompt
Table 1: Sub-question structure generated by the QueryPlanner
Figure 2: (a) DAG execution: independent sub-questions execute in Wave 1; dependent questions in subsequent waves. (b) Verification-driven iteration: Iteration 1 identifies incomplete results, triggering replanning; Iteration 2 achieves sufficient completeness for synthesis.
Condition
Threshold
Rationale
Ready for Synthesis
80% complete
Sufficient sub-questions answered
High Confidence
75% conf, 50% complete
High reliability despite partial coverage
Diminishing Returns
< 5% improvement
Further iteration yields minimal gain
Token Budget
1M tokens
Hard cost limit
Max Iterations
3 iterations
Hard iteration limit
Table 2: Stop conditions for orchestration termination
Tier
Agent
Tools
Key Capabilities
1: Data
RAG
13
Semantic, keyword, and hybrid retrieval; metadata filtering
Web Search
4
General and AI-powered search, news retrieval
Financial
7
Stock quotes, technical indicators, fundamentals
Competitor
11
Market positioning, benchmarks, competitor news
2: Analysis
Analysis
20
Survey analytics, financial and competitor analysis
Reasoning
24
Cross-domain reasoning with RAG, web, and financial tools
Table 3: Agent taxonomy with tool allocation across MCP servers (42 unique tools total)
Figure 3: (a) Token usage breakdown by orchestration phase for a typical query. Execution dominates at 61%, while verification and synthesis remain efficient. (b) Completeness scores by query category across methods. VMAO shows consistent improvements, with largest gains on Strategic Assessment (+53%).
Method
Completeness
Source Quality
Avg Tokens
Avg Time (s)
Single-Agent
3.1
2.6
100K
165
Static Pipeline
3.5
3.2
350K
420
VMAO (Ours)
4.2
4.1
850K
900
Table 4: Comparison of orchestration methods on market research tasks. Completeness and Source Quality are co-scored by LLM and human evaluators (1–5 scale, higher is better).
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Parameter
Default
Description
max_iterations
3
Maximum replanning iterations
token_budget
1M
Maximum tokens before stopping
ready_threshold
0.8
Completeness ratio for synthesis
high_confidence
0.75
Confidence threshold for early stop
diminishing_returns
0.05
Minimum improvement to continue
max_concurrent
3
Parallel agent executions
Appendix
Table 5: Configuration parameters for VMAO orchestration