discoverymediumemerging

Semantic Capability Matching Pattern

Finding agents by natural language description rather than exact capability tags

Overview

The Challenge

Traditional capability matching requires exact keyword matches. When requesters describe needs in natural language, they may not use the same terms agents used to describe their capabilities.

The Solution

Use embedding models to create semantic representations of both capability descriptions and queries. Match based on vector similarity, enabling fuzzy, intent-based discovery.

When to Use
  • Large agent pools with diverse capabilities
  • User-facing agent selection interfaces
  • When capability taxonomies are incomplete
  • Cross-domain agent discovery
When NOT to Use
  • Small, well-defined agent pools
  • When exact capability matching is required
  • Latency-critical discovery paths

Trade-offs

Advantages
  • +Handles natural language queries
  • +Discovers unexpected matches
  • +No need for perfect taxonomy
  • +Improves over time with feedback
Considerations
  • −Embedding computation overhead
  • −May return false positives
  • −Requires vector database
  • −Less predictable than exact match
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Deep Dive

Overview

Semantic Capability Matching uses embeddings to find agents based on the meaning of requests, not just keyword matches. This enables more natural and flexible discovery.

How It Works

1. Index Agent Capabilities

Convert each agent's capability description into a vector embedding.

2. Embed the Query

Convert the user's natural language request into the same vector space.

Find agents whose capability vectors are closest to the query vector.

4. Rank and Filter

Return top matches above a similarity threshold.

Embedding Strategies

Single Description

One embedding per agent covering all capabilities.

  • Simple but loses granularity

Per-Capability Embeddings

Separate embedding for each distinct capability.

  • More precise matching
  • Larger index

Hierarchical Embeddings

Embeddings at multiple granularity levels.

  • Best of both worlds
  • More complex

Improving Match Quality

Feedback Loop

Track which matches led to successful collaborations.

Query Expansion

Augment queries with synonyms or related terms.

Hybrid Matching

Combine semantic search with keyword filters.

Example Queries

Query Matches
"I need help analyzing financial data" Data Analysis Agent, Finance Expert Agent
"Translate this to Spanish" Translation Agent, Multilingual Agent
"Check if this code has bugs" Code Review Agent, Testing Agent

Code Examples

Semantic agent discoverypython
from sentence_transformers import SentenceTransformer
import numpy as np

class SemanticAgentMatcher:
    def __init__(self):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        self.agent_embeddings = {}

    def index_agent(self, agent_id, capability_description):
        """Create embedding for agent's capabilities."""
        embedding = self.model.encode(capability_description)
        self.agent_embeddings[agent_id] = {
            'embedding': embedding,
            'description': capability_description
        }

    def find_agents(self, query, top_k=5, threshold=0.7):
        """Find agents matching natural language query."""
        query_embedding = self.model.encode(query)

        scores = []
        for agent_id, data in self.agent_embeddings.items():
            similarity = np.dot(query_embedding, data['embedding'])
            if similarity >= threshold:
                scores.append((agent_id, similarity, data['description']))

        # Return top matches sorted by similarity
        return sorted(scores, key=lambda x: x[1], reverse=True)[:top_k]
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Considerations

Semantic matching is probabilistic. For critical paths, combine with explicit capability checks after discovery.

PreviousAgent Service Mesh Pattern
NextAgent Registry Pattern
Dimension Scores
Safety
3/5
Accuracy
4/5
Cost
3/5
Speed
3/5
Implementation
Complexitymedium
Implementation Checklist
Embedding model
Vector database
Capability descriptions
0/3 complete
Tags
discoverysemanticembeddingsnlpsearchmatching

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