AgentGenome for Cohere Users
Profile Your Cohere Agents. Make RAG Behaviors Portable.
Capture enterprise RAG patterns in substrate-independent genomes
2,000+ developers • Portable across 12+ LLMs • No credit card required
Why Cohere Users Need AgentGenome
Cohere excels at enterprise RAG applications. AgentGenome captures how your agents retrieve, reason, and respond—so these behaviors work across any retrieval stack.
Enterprise RAG Lacks Behavioral Analytics
Your RAG pipeline retrieves documents, but how does your agent actually use them? Without profiling, retrieval quality vs. generation quality is a mystery.
RAG Optimization Is Trial and Error
Adjusting chunking, embedding, or retrieval count is guesswork without behavioral baselines. AgentGenome shows exactly how changes affect agent behavior.
Switching Embedding Models Breaks Things
Upgrading embeddings can silently break your RAG agent's behaviors. AgentGenome detects drift from baseline so you catch issues before production.
Better RAG Stacks Launch Constantly
New retrieval models and vector databases emerge weekly. Can you upgrade without losing your carefully tuned RAG behaviors?
"Sound familiar?"
AgentGenome Solves Every Problem
Add 3 lines of code. Capture your agent's behavioral DNA. Deploy anywhere.
import cohere
from agentgenome import profile
co = cohere.Client(api_key="YOUR_KEY")
@profile(genome_id="rag-assistant")
def rag_query(query: str, documents: list):
response = co.chat(
model="command-r-plus",
message=query,
documents=documents
)
return response.text
# RAG behavioral patterns captured automaticallyWhat You Get
- Behavioral profiling without code changes
- 35% average token savings
- Real-time drift detection
- Substrate-independent genome export
- Multi-provider deployment ready
Profile Once. Deploy Anywhere.
RAG profiles port to any retrieval stack. Capture how your Cohere agent uses retrieved context and apply those patterns to upgraded embeddings, new vector DBs, or different LLMs.
Your Cohere Agents Deserve Freedom
You've invested months optimizing Cohere prompts. What happens when costs rise, performance drops, or a better model launches?
✗Without AgentGenome
- •Start over. Rebuild every prompt from scratch.
- •Lose months of behavioral optimization.
- •4-6 weeks of engineering per migration.
- •$47K+ average migration cost.
- •40%+ behavioral drift during migration.
With AgentGenome
- ✓Export your behavioral genome in one click.
- ✓Import to GPT + RAG, Claude + RAG, Llama + RAG, or any provider.
- ✓Keep your optimizations. Zero rework.
- ✓95%+ behavioral consistency guaranteed.
- ✓Hours, not weeks. Included in Pro tier.
# Export RAG behavioral genome
from agentgenome import genome
# Capture Cohere RAG patterns
genome.export('rag-assistant.genome')
# Deploy with new embedding model
genome.import_to('rag-assistant.genome', config={'embeddings': 'new-model'})
# Or switch LLM entirely
genome.import_to('rag-assistant.genome', provider='anthropic')Deploy your Cohere genome on any supported provider:
Profile once. Deploy anywhere. Never locked in.
Build Cohere agents Once.
Deploy on Any LLM.
RAG profiles port to any retrieval stack. Capture how your Cohere agent uses retrieved context and apply those patterns to upgraded embeddings, new vector DBs, or different LLMs.
Your Agent Genome
Behavioral DNA captured in universal format
Profile Your Cohere agents
Add 3 lines of code. Capture behavioral DNA automatically.
import cohere
from agentgenome import profile
co = cohere.Client(api_key="YOUR_KEY")
@profile(genome_id="rag-assistant")
def rag_query(query: str, documents: list):
response = co.chat(
model="command-r-plus",
message=query,
documents=documents
)
return response.text
# RAG behavioral patterns captured automaticallyYour agents become substrate-independent
Profile today, deploy on any LLM tomorrow. Your optimizations travel with you.
Real Results with AgentGenome
How LegalSearch Made RAG Behaviors Portable
The Challenge
LegalSearch's contract analysis agent used Cohere for RAG. When a superior legal-specific embedding model launched, they needed to upgrade without breaking the carefully tuned retrieval-to-generation behaviors developed over a year.
The Solution
AgentGenome profiled how the agent used retrieved context—citation patterns, relevance weighting, and synthesis styles. This RAG genome was applied when upgrading the embedding model.
"We upgraded our entire retrieval stack without users noticing. The agent behaves the same, just with better source material."
— Jennifer Walsh, Head of Product, LegalSearch
Without vs With AgentGenome
| Aspect | Without AgentGenome | With AgentGenome |
|---|---|---|
| Debugging Time | 4+ hours per incident | 52 minutes average (-78%) |
| Token Efficiency | Unknown waste | 35% average savings |
| Behavioral Visibility | Black box | Full trait analysis |
| Drift Detection | Discover in production | Catch before deployment |
| Agent Portability | 🔒 Locked to Cohere | 🔓 Deploy on any LLM |
| Migration Time | 4-6 weeks per provider | Hours with genome export |
| Migration Cost | $47K+ engineering | Included in Pro tier |
| Multi-Provider Strategy | Rebuild for each | One genome, all providers |
| Future-Proofing | Start over when models change | Take your genome with you |
| Vendor Negotiation | No leverage (locked in) | Full leverage (can leave) |
The Cost of Waiting
💸 Financial Lock-In
- Cohere pricing has increased multiple times since launch
- Without portable profiles, you pay whatever they charge
- Migration estimate without AgentGenome: $47K and 8 weeks
⚠️ Strategic Lock-In
- Better alternatives might exist—but can you actually switch?
- Your competitors are profiling for portability right now
- When you need to migrate, will you be ready?
🔒 The Vendor Lock-In Tax
- 4-6 weeks of engineering to migrate unprofiled agents
- 40%+ behavioral drift during manual migration
- Zero leverage in pricing negotiations
📉 Competitive Disadvantage
- Competitors with portable profiles ship 80% faster
- They negotiate contracts with leverage—you don't
- They test new models in hours; you take months
"Every day without profiling locks you deeper into Cohere."
When Cohere raises prices or a better model launches, will you be ready to leave?
What You'll Achieve with AgentGenome
Real metrics from Cohere users who profiled their agents
Before AgentGenome
- • Debugging: 4+ hours per incident
- • Migration: 4-6 weeks per provider
- • Token waste: Unknown
- • Drift detection: In production
- • Vendor leverage: None
After AgentGenome
- • Debugging: 52 minutes average
- • Migration: Hours with genome export
- • Token savings: 35% average
- • Drift detection: Before deployment
- • Vendor leverage: Full (can leave anytime)
Already Locked Into Cohere?
Here's how to escape with your behavioral DNA intact
Profile Your Current Agent
Add 3 lines of code to capture your Cohere agent's behavioral DNA. No changes to your existing logic.
Export Your Genome
One command exports your substrate-independent genome. It works on any LLM provider, not just Cohere.
Deploy Anywhere
Import your genome to Claude, Gemini, Llama, or any provider. 95%+ behavioral consistency, zero rework.
Zero-Downtime Migration Promise
AgentGenome's migration assistant guides you through the process. Profile your current agent while it's running, export the genome, and deploy to a new provider—all without touching your production system until you're ready.
Start Free. Unlock Portability with Pro.
Most Cohere users choose Pro for multi-provider genome sync. Start free and upgrade when you need portability.
| Portability Features | Free | Pro | Enterprise |
|---|---|---|---|
| Genome Export | JSON only | JSON + YAML | All formats |
| Multi-Provider Sync | — | ✓ | ✓ + Custom |
| Migration Assistant | — | ✓ | ✓ + SLA |
| Custom Substrate Adapters | — | — | ✓ |
Frequently Asked Questions
Everything you need to know about AgentGenome for Cohere