Preserving multimodal signals across agent boundaries is necessary for accurate cross-modal reasoning, but it is not sufficient. We show that modality-native routing in Agent-to-Agent (A2A) networks improves task accuracy by 20 percentage points over text-bottleneck baselines, but only when the downstream reasoning agent can exploit the richer context that native routing preserves. An ablation replacing LLM-backed reasoning with keyword matching eliminates the accuracy gap entirely (36% vs. 36%), establishing a two-layer requirement: protocol-level routing must be paired with capable agent-level reasoning for the benefit to materialize. We present MMA2A, an architecture layer atop A2A that inspects Agent Card capability declarations to route voice, image, and text parts in their native modality. On CrossModal-CS, a controlled 50-task benchmark with the same LLM backend, same tasks, and only the routing path varying, MMA2A achieves 52% task completion accuracy versus 32% for the text-bottleneck baseline (95% bootstrap CI on ΔTCA: [8, 32] pp; McNemar's exact p=0.006). Gains concentrate on vision-dependent tasks: product defect reports improve by +38.5 pp and visual troubleshooting by +16.7 pp. This accuracy gain comes at a 1.8× latency cost from native multimodal processing. These results suggest that routing is a first-order design variable in multi-agent systems, as it determines the information available for downstream reasoning.
Figures & tables
Figure 1: MMA2A deployment architecture. The Modality-Aware Router inspects Agent Card capabilities and forwards parts in their native modality. Annotations show the A2A Part type used for each route.
Figure 2: Information flow comparison. Text-BN (top) transcodes all non-text parts, losing prosodic and spatial signals. MMA2A (bottom) preserves native modality, delivering richer evidence to the shared LLM reasoning step.
Category
n
Voice
Image
Text
Product defect report
13
✓
✓
✓
Assembly guidance
12
✓
✓
Visual troubleshooting
12
✓
✓
Warranty claim
13
✓
✓
✓
Table 1: CrossModal-CS task categories and modality requirements.
System
TCA (%)
Latency (s)
BW (KB/task)
Native routing (%)
Text-BN
32.0
7.19 ± 4.46
329
50.5
MMA2A (Ours)
52.0
13.04 ± 6.39
330
81.7
Δ
+20.0 pp
+5.85 s
+0.5%
+31.2 pp
Table 2: Main results on CrossModal-CS (50 paired tasks). MMA2A achieves substantially higher accuracy at the cost of increased latency from native multimodal processing.
Decision Method
Text-BN TCA
MMA2A TCA
Δ
Keyword matching (heuristic)
36%
36%
0 pp
LLM reasoning (Gemini)
32%
52%
+20 pp
Table 3: Ablation: keyword matching vs. LLM reasoning for the final decision step. With keyword matching, the routing strategy has no effect on accuracy.
Figure 3: Causal diagram of the two-layer requirement. Routing strategy determines input fidelity, which enables accuracy gains—but only when reasoning capability (moderator) can exploit the richer evidence. The ablation confirms that blocking the moderator eliminates the effect.
Category
n
Text-BN
MMA2A
Δ
Product defect report
13
7.7
46.2
+38.5
Visual troubleshooting
12
75.0
91.7
+16.7
Assembly guidance
12
41.7
58.3
+16.6
Warranty claim
13
7.7
15.4
+7.7
Table 4: TCA (%) by task category. The largest gains appear in vision-dependent categories where text transcoding loses the most signal.
Modality
MMA2A
Text-BN
Native
Transcode
Native
Transcode
Voice
40 (100%)
0 (0%)
0 (0%)
40 (100%)
Image
28 (70%)
12 (30%)
0 (0%)
40 (100%)
Text
110 (80%)
28 (20%)
110 (80%)
28 (20%)
Total
178 (82%)
40 (18%)
110 (50%)
108 (50%)
Table 5: Routing decisions by modality. MMA2A achieves 100% native routing for voice and 70% for images; Text-BN forces all non-text parts through transcoding.
Category
Mean (s)
Median (s)
Text-BN
MMA2A
Text-BN
MMA2A
Assembly guidance
9.67
9.66
8.47
8.81
Product defect report
3.96
16.55
3.74
18.19
Visual troubleshooting
11.03
14.51
9.50
13.61
Warranty claim
4.61
11.31
4.66
8.77
Overall
7.19
13.04
5.93
9.42
Table 6: Latency statistics (seconds) by category. MMA2A is consistently slower due to native multimodal processing, with the cost proportional to the complexity of the multimodal input.
Failure Mode
n
Layer
Description
Policy lookup failure
11
Reasoning
Correct evidence, wrong policy match
Action granularity confusion
6
Reasoning
Semantically adjacent action selected
Overconfident visual grounding
4
Routing × Reasoning
Richer input triggers over-action
Insufficient context
3
Routing
Too little evidence reaches the agent
Table 7: MMA2A failure modes, classified by manual inspection of predicted-vs-expected action pairs and agent response traces. The Layer column identifies which architectural layer is responsible for the failure.
Figure 4: Information topologies induced by routing strategy. (a) Text-BN funnels all modalities through a lossy text bottleneck, imposing a uniform but degraded evidence surface on all agents. (b) MMA2A routes each modality natively to a capable agent, creating a heterogeneous topology with higher-fidelity inputs. Error modes from Table 7 are mapped to the architectural layer responsible: the vast majority of remaining failures occur downstream of routing, in the reasoning layer.