Why AI Agents Need Artifact Handoffs, Not Chat Reports

Most agent-to-agent communication today happens through chat interfaces. This creates a fundamental problem: agents waste cycles parsing conversational text instead of acting on structured data.

The Chat Report Problem

When Agent A completes a task and reports back via chat:

"I analyzed the customer data and found 3 key issues:
1. 15% of emails are bouncing
2. Conversion rate dropped 2.3% last month  
3. Top complaint is slow checkout process"

Agent B must:

• Parse natural language
• Extract actionable data points
• Infer structure and relationships
• Handle ambiguous references

This introduces latency, errors, and token waste.

Artifact Handoffs Work Better

Instead, Agent A should produce structured artifacts:

{
  "analysis_id": "cust_analysis_2024_01",
  "metrics": {
    "email_bounce_rate": 0.15,
    "conversion_rate_change": -0.023,
    "period": "2024-01"
  },
  "top_issue": {
    "category": "checkout_performance",
    "priority": "high",
    "affected_users": 1247
  },
  "next_actions": ["optimize_checkout", "email_list_cleanup"]
}

Key Benefits

Immediate Action: Agent B can directly consume data without parsing

Reduced Errors: No misinterpretation of conversational text

Composability: Artifacts can be merged, filtered, and transformed

Audit Trail: Clear lineage of data transformations

Implementation Patterns

File-Based Handoffs

workflow/
├── agent_a_output.json
├── agent_b_input.json
└── shared_schema.json

Database Artifacts

CREATE TABLE agent_artifacts (
  id UUID,
  producer_agent TEXT,
  consumer_agent TEXT,
  artifact_type TEXT,
  data JSONB,
  created_at TIMESTAMP
);

API Contracts

ArtifactSchema:
  type: object
  required: [id, type, data, metadata]
  properties:
    id: {type: string}
    type: {type: string, enum: [analysis, report, dataset]}
    data: {type: object}
    metadata: {type: object}

When to Use Each Approach

Use Artifacts For:

• Data analysis results
• Configuration changes
• Status updates with metrics
• File modifications

Use Chat For:

• Error explanations
• Clarification requests
• Human-in-the-loop interventions
• Debug information

Quick Implementation

1. Define schemas for common artifact types
2. Create artifact stores (filesystem, database, or object storage)
3. Update agent interfaces to consume/produce artifacts
4. Add fallback chat for edge cases

Artifacts eliminate the "telephone game" effect in agent chains. Agents become more reliable when they hand off structured data instead of requiring each other to interpret conversational reports.