How unified entity models and decision ledgers are replacing fragmented GTM data stacks - and what it actually takes to build one
Last updated: January 2026 | Reading time: 20 minutes
This is part of a 3-post series on AI infrastructure for GTM:
1. Context Graphs - The data foundation (memory, world model (you are here)
2. Agent Harness - The coordination infrastructure (policies, audit trails)
3. Long Horizon Agents - The capability that emerges when you have both
Quick Answer: What is a Context Graph for GTM?
A context graph is a unified data architecture that connects every entity in your go-to-market ecosystem - companies, people, deals, activities, and outcomes - into a single queryable structure that AI agents can reason over.
In December 2025, Foundation Capital called context graphs "AI's trillion-dollar opportunity" - arguing that enterprise value is shifting from "systems of record" to "systems of agents." The new crown jewel isn't the data itself; it's a living record of decision traces stitched across entities and time, where precedent becomes searchable.
Best Context Graph by Use Case
Best for SMB revenue teams (50-200 employees): A lightweight implementation using PostgreSQL with good indexing, focusing on Company → Person → Employment relationships. You don't need a graph database to start—most B2B SaaS teams can get to first value in 4 weeks with existing infrastructure.
Best for mid-market with AI agents: A 5-layer architecture combining entity resolution, activity ledgers, and policy engines. This enables AI marketing ops agents to make autonomous decisions with full traceability. Teams report saving 40-60 minutes daily per rep on research and routing.
Best for enterprise RevOps: A full context graph with multi-vendor identity resolution, computed columns for AI efficiency, and CRM bidirectional sync. Companies at this stage typically see 30% improvement in win rates and 300% improvement in meeting booking rates from high-intent accounts.
Best use case for context graphs: Replacing the fragmented "intent signal → manual routing → CRM update" workflow with a closed-loop system where every decision (who to contact, what to say, when to engage) is logged, executed, and evaluated automatically.
Why context graphs matter now: Traditional GTM tools give you signals without structure. You get 1,000 website visitors but no way for AI to understand that visitor A works at company B which has deal C with champion D who just changed jobs. Context graphs solve this by making relationships first-class citizens in your data model.
What this guide covers: This is the definitive guide to context graphs specifically for go-to-market teams. While most context graph content focuses on general enterprise use cases, we'll show you exactly how to build a world model for your revenue ecosystem - with real entity examples, GTM-specific decision traces, and implementation guidance.
The Problem: GTM Data is a Mess of Disconnected Signals
Every revenue team knows this pain:
- Your website intent data shows Company X visited your pricing page
- Your Bombora research signals show they're researching your category
- Your CRM shows you talked to them 6 months ago
- Your LinkedIn shows their VP of Sales just got promoted
- Your outbound tool has 3 SDRs sending conflicting messages
None of these systems talk to each other. And when you try to add AI agents on top, they hallucinate because they lack the connected context to make good decisions.
This is the fundamental problem context graphs solve: creating a world model for your go-to-market ecosystem that AI can actually reason over.
What Makes a Context Graph Different from a Data Warehouse?
| Aspect | Data Warehouse | CDP | Context Graph |
|---|
| Primary unit | Tables/rows | User profiles | Entities + relationships |
| Query pattern | SQL aggregations | Audience segments | Graph traversal |
| Real-time | Batch (hours/days) | Near real-time | Real-time events |
| AI readiness | Requires heavy transformation | Limited to known schemas | Native entity resolution |
| Decision logging | Not built-in | Not built-in | Immutable ledger layer |
| Best for | Reporting | Marketing automation | AI agent orchestration |
The key insight:
Data warehouses store facts. Context graphs store meaning.When an AI agent asks "Who should I contact at Acme Corp about our new product?", a data warehouse returns rows. A context graph returns:
- The buying committee with roles and relationships
- Historical engagement with each person
- Related deals and their outcomes
- The last 10 decisions made about this account and what happened
The 5-Layer Context Graph Architecture
After building AI agents for GTM that actually work in production, we've converged on a 5-layer architecture:
Layer 1: Data Layer (The World Model)
This is your unified entity graph containing:
Core Entities:
- Company - Firmographic data, technographic signals, ICP scoring
- Person - Contact data, role identification, social presence
- Employment - Links people to companies with titles, seniority, tenure
- Deal - Opportunities with stages, amounts, probability
- Activity - Every touchpoint: emails, calls, meetings, page views
- Audience - Dynamic segments based on rules or ML models
The magic is in the relationships. Unlike flat CRM records, a context graph knows that:
- Person A works at Company B
- Person A is champion on Deal C
- Person A previously worked at Company D (which is your customer)
- Company B competes with Company E
This relationship-first structure is what enables person-based signals to actually drive intelligent action.
Real GTM Example: The Buying Committee Query
When your AI agent asks "Who should I contact at Acme Corp?", here's what the context graph returns:
Company: Acme Corp (acme.com)
├── ICP Tier: 1 (Strong Fit)
├── Intent Score: 85/100
├── Recent Activity: Pricing page (3x), Case studies (2x)
│
├── Buying Committee:
│ ├── Sarah Chen (VP of Sales) — CHAMPION
│ │ ├── LinkedIn: Active, 5K followers
│ │ ├── Previous company: [Your Customer]
│ │ └── Last contact: 45 days ago (email opened)
│ │
│ ├── Mike Rodriguez (CRO) — DECISION MAKER
│ │ ├── Started role: 3 months ago (new hire signal)
│ │ └── Last contact: Never
│ │
│ └── Jessica Liu (Director RevOps) — INFLUENCER
│ ├── Tech stack owner
│ └── Last contact: Demo request form (2 weeks ago)
│
├── Related Deals:
│ └── Closed Lost: $45K (6 months ago, "timing")
│
└── Similar Accounts (won):
└── Beta Corp, Gamma Inc (same industry, similar size)
This is what it means to have a world model for GTM. The agent doesn't just know that someone visited your website - it knows the full context of who they are, how they relate to the account, and what happened before.
Layer 2: Ledger Layer (Decision Memory)
Every decision your GTM system makes gets logged immutably:
DecisionRecord {
timestamp: "2026-01-15T10:30:00Z"
decision_type: "outreach_channel_selection"
entity: "person:uuid-123"
context_snapshot: { ... full entity state at decision time ... }
decision: "linkedin_message"
reasoning: "High LinkedIn engagement score, email bounced previously"
policy_version: "v2.3.1"
outcome: null // Filled in later when we observe result
}
Why this matters: When your AI orchestrator makes a decision, you need to know:
- What it decided
- Why it decided that
- What information it had at the time
- What happened afterward Without a ledger, AI agents become black boxes. With a ledger, you get full auditability and - critically - the ability to learn from outcomes.
Layer 3: Policy Layer (The Rules Engine)
Policies are versioned rules that govern agent behavior:
yaml
policy_name: "outreach_timing"
version: "2.3.1"
rules:
- condition: "prospect.seniority == 'C-Level'"
action: "delay_until_business_hours"
reasoning: "Executives prefer professional timing"
- condition: "prospect.recent_activity.includes('pricing_page')"
action: "prioritize_immediate_outreach"
reasoning: "High intent signals decay quickly"
The policy layer sits between raw AI capabilities and production execution. It encodes your business logic, compliance requirements, and learnings from past outcomes.
Key principle: Policies evolve. When the ledger shows that a certain approach isn't working, you update the policy—and the version history tells you exactly what changed and when.
Layer 4: Agent API Layer
This is the interface where AI agents interact with the context graph:
- Query API - "Get full context for Company X including buying committee, recent activity, and similar accounts"
- Decision API - "Log that I'm deciding to send an email to Person Y"
- Action API - "Execute this email send through integration Z"
- Feedback API - "Record that the email was opened/replied/bounced" The API layer abstracts the complexity of the underlying graph, presenting AI agents with clean interfaces that match how they reason about GTM problems.
Layer 5: External Systems Layer
Context graphs don't replace your existing tools—they unify them:
- CRM integration - Salesforce, HubSpot records flow in and out
- Engagement platforms - Outreach, Salesloft sequences sync bidirectionally
- Data vendors - Contact database enrichment from Clearbit, ZoomInfo, Apollo
- Intent providers - First-party web, second-party social, third-party research signals
The integration layer handles the messy reality of enterprise GTM stacks while maintaining the clean entity model internally.
The Identity Resolution Problem (And How Context Graphs Solve It)
Before you can build a context graph, you need to answer: "Is this the same person/company across all my systems?"
This is harder than it sounds:
- CRM has "Acme Corp"
- Website tracking has "acme.com"
- LinkedIn has "Acme Corporation"
- Email domain is "acme.io"
Multi-vendor consensus approach: Instead of trusting any single data provider, context graphs use a waterfall of vendors and vote on matches:
- Query Clearbit, ZoomInfo, PDL, Demandbase for the same entity
- Compare returned data across vendors
- Accept matches where 2+ vendors agree
- Flag conflicts for human review
This approach achieves ~90% accuracy on identity resolution - good enough for AI agents to operate autonomously while flagging edge cases.
Why Computed Columns Matter for AI Efficiency
Here's a non-obvious insight from building production AI systems: Raw data is too expensive for LLMs to process.
If you send an AI agent the full activity history for a company (1,000+ events), you're burning tokens and getting worse decisions. The model gets lost in noise.
Solution: Computed columns that pre-digest data. Instead of:
json
{
"activities": [
{"type": "page_view", "url": "/pricing", "timestamp": "..."},
{"type": "page_view", "url": "/features", "timestamp": "..."},
// ... 998 more events
]
}
```
The context graph provides:
```json
{
"engagement_score": 85,
"buying_stage": "evaluation",
"last_pricing_view": "2 days ago",
"total_sessions_30d": 12,
"key_pages_viewed": ["pricing", "vs-competitor", "case-studies"],
"engagement_trend": "increasing"
}
The AI agent gets the meaning without the noise. This reduces token consumption by 10-100x while actually improving decision quality.
The Decision Loop: From Signals to Outcomes
Traditional GTM is linear: Signal → Action → Hope.
Context graph-powered GTM is a closed loop:
Three Levels of Evaluation
Not all decisions are equal. Context graphs support evaluation at three levels:
Turn-Level (Individual Actions)
- Did this specific email get opened?
- Did this LinkedIn message get a reply?
- Was this the right person to contact?
Thread-Level (Conversation Sequences)
- Did this outreach sequence generate a meeting?
- How many touches did it take?
- Which channels performed best for this persona?
Outcome-Level (Business Results)
- Did this account become a customer?
- What was the deal value?
- What was the time from first touch to close?
Evaluation connects decisions to outcomes across time:
The email you sent on Day 1 contributed to the meeting on Day 14 which contributed to the closed deal on Day 90. Context graphs maintain these connections so you can attribute outcomes to the decisions that actually mattered.
Context Graphs vs. 6sense, Demandbase, and Traditional ABM
If you're evaluating ABM platforms, you might wonder: don't 6sense and Demandbase already provide intent data and orchestration?
| Capability | 6sense/Demandbase | Context Graph Approach |
|---|
| Intent signals | Yes | Yes (multi-source) |
| Account identification | Yes | Yes (with identity resolution) |
| Audience segmentation | Yes | Yes (real-time) |
| AI-powered actions | Limited | Full agent autonomy |
| Decision logging | No | Immutable ledger |
| Outcome attribution | Partial | Full loop |
| Custom entity models | No | Fully extensible |
| Token-efficient AI | No | Computed columns |
|
The fundamental difference: Traditional ABM platforms are signal providers. Context graphs are reasoning infrastructure.
You can (and should) feed 6sense intent data into your context graph. The graph provides the structure for AI agents to actually act on those signals intelligently.
Building Your Own Context Graph: Key Decisions
If you're building GTM infrastructure, here are the critical choices:
1. Entity Model Design
Start with Company → Person → Employment as your core triangle. Everything else connects to these three entities.
Don't:
- Create separate "Lead" and "Contact" entities (they're the same person)
- Store activities as disconnected events (link them to entities)
- Treat accounts as flat records (model the buying committee)
2. Identity Resolution Strategy
Decide your accuracy vs. speed tradeoff:
- Fast and approximate: Single-vendor matching (70% accuracy)
- Accurate and slower: Multi-vendor consensus (90% accuracy)
- Maximum accuracy: Human-in-the-loop for high-value accounts (98%+)
3. Ledger Granularity
What gets logged?
- Minimum: All AI agent decisions
- Recommended: All decisions + context snapshots
- Maximum: Every state change in the system More logging = better learning, but higher storage costs.
4. Policy Versioning
Treat policies like code:
- Git-versioned rule definitions
- Rollback capability for bad deployments
- A/B testing between policy versions
How to Get Started: 4-Week Implementation Path
Based on our experience and industry frameworks, here's a practical path to your first context graph.
What to Expect: Effort vs. Outcomes
| Week | Effort Required | What You Get |
|---|
| Week 1 | 20-30 hours (data eng) | Core entity model, can query buying committees |
| Week 2 | 15-20 hours (data eng + RevOps) | Identity resolution, ~90% match accuracy |
| Week 3 | 10-15 hours (RevOps) | Activity tracking, intent signals flowing |
| Week 4 | 15-20 hours (data eng) | First AI agent connected, decision logging |
Total investment: ~60-85 hours of specialized work over 4 weeks.
By week 4 you should see:
- AI agents answering "Who should we contact at Company X?" with full context
- 40-60 minutes saved per rep daily on research and routing
- Foundation for outcome-based learning (though outcomes take time to accumulate) This isn't magic—it's infrastructure. The payoff compounds as your ledger accumulates decision traces and outcomes.
Week 1: Entity Model Foundation
Start with the core triangle: Company → Person → Employment
sql
-- Minimum viable schema
CREATE TABLE company (
id UUID PRIMARY KEY,
domain TEXT UNIQUE,
name TEXT,
icp_tier TEXT,
employee_count INT
);
CREATE TABLE person (
id UUID PRIMARY KEY,
full_name TEXT,
linkedin_handle TEXT,
email TEXT
);
CREATE TABLE employment (
id UUID PRIMARY KEY,
person_id UUID REFERENCES person(id),
company_id UUID REFERENCES company(id),
title TEXT,
seniority TEXT, -- C-Level, VP, Director, Manager, IC
is_current BOOLEAN,
started_at TIMESTAMP
);
Don't over-engineer. You can run effective AI agents on PostgreSQL with good indexing. Graph databases add value later when you need complex traversals.
Week 2: Identity Resolution Pipeline
Connect your data sources and start matching entities:
- Ingest from CRM - Pull companies, contacts, deals from Salesforce/HubSpot
- Enrich with vendors - Query Clearbit, ZoomInfo, or Apollo for additional data
- Match and merge - Use domain matching for companies, email + name matching for people
- Flag conflicts - Queue low-confidence matches for human review Start with domain-based company matching (highest accuracy) before tackling person matching.
Week 3: Activity and Intent Layer
Add the engagement signals that make the graph dynamic:
sql
CREATE TABLE activity (
id UUID PRIMARY KEY,
entity_type TEXT, -- 'person' or 'company'
entity_id UUID,
activity_type TEXT, -- 'page_view', 'email_open', 'meeting', etc.
payload JSONB,
occurred_at TIMESTAMP
);
-- Computed column example
CREATE VIEW company_engagement AS
SELECT
company_id,
COUNT(*) FILTER (WHERE occurred_at > NOW() - INTERVAL '30 days') as sessions_30d,
COUNT(DISTINCT entity_id) FILTER (WHERE entity_type = 'person') as known_visitors,
MAX(occurred_at) as last_activity
FROM activity
GROUP BY company_id;
Week 4: Decision Logging and First Agent
Add the ledger layer and connect your first AI agent:
1. Create decision table - Log every agent decision with context snapshot
2. Build query API - Simple endpoint: "Get full context for company X"
3. Connect one agent - Start with a single use case (e.g., meeting prep, outreach prioritization)
4. Measure outcomes - Track what the agent decided vs. what actually happened
First milestone: An AI agent that can answer "Who should we contact at Company X and why?" with full traceability.
How Warmly Implements Context Graphs
At Warmly, we built our context graph to power AI agents that handle inbound, outbound, and marketing ops autonomously. We're sharing what works (and what's still hard) because context graphs are emerging infrastructure - everyone's learning.
Our data layer includes:
Our ledger captures:
- Every orchestration decision
- Every AI-generated message
- Every routing choice
- Every outcome (reply, meeting, deal)
Our policy layer encodes:
- ICP definitions and scoring
- Buying committee identification rules
- Channel selection preferences
- Timing and frequency constraints
What We've Seen Work
Teams using our context graph infrastructure report:
- 20% more pipeline capacity - SDR teams cover more accounts without adding headcount
- 50% higher close rates on MQLs from context-enriched routing vs. standard form fills
- 30% faster sales cycles when AI surfaces the right buying committee members upfront
- Some teams have replaced the work of 1-2 SDRs with automated outreach to high-intent accounts
Where Context Graphs Are Still Hard (Honest Assessment)
Let's be real about the limitations:
Data quality requires ongoing work. B2B contact data decays 25-30% annually. Job changes, title updates, company acquisitions - the graph needs constant maintenance. We've invested heavily in multi-vendor consensus to stay accurate, but it's not "set and forget."
CRM sync takes configuration. Every Salesforce and HubSpot instance is customized. Getting bidirectional sync right - especially with custom objects and complex ownership rules - takes time. Budget 2-3 weeks for production-grade CRM integration.
Trust builds gradually. AI agents making autonomous decisions feels risky. Most teams start with "recommend but don't act" mode before enabling full autonomy. This is healthy - you should understand what the AI would do before letting it do it.
Not a fit for pure PLG. If you don't have a sales team, context graphs add complexity you don't need. They're built for teams with SDRs, AEs, and outbound motions.
The result: AI agents that can answer "Who should we contact at this account, what should we say, and why?" - with full auditability of how they reached that conclusion. But getting there takes investment.
FAQs: Context Graphs for GTM
What is a context graph in the context of B2B sales?
A context graph is a unified data structure that represents all entities (companies, people, deals, activities) and their relationships in your go-to-market ecosystem. Unlike flat CRM records, context graphs model the connections between entities - like which people work at which companies, who the buying committee is, and how past activities relate to current opportunities. This structure enables AI agents to reason about complex GTM scenarios rather than just retrieving individual records.
How is a context graph different from a Customer Data Platform (CDP)?
CDPs are designed for marketing automation around known user profiles. Context graphs are designed for AI agent orchestration across the full GTM motion. Key differences:
- CDPs organize around user profiles; context graphs organize around entity relationships
- CDPs segment audiences; context graphs enable graph traversal queries
- CDPs don't typically log AI decisions; context graphs include an immutable ledger layer
- CDPs are optimized for campaign execution; context graphs are optimized for autonomous agent reasoning
What data sources feed into a GTM context graph?
A comprehensive context graph ingests:
- First-party signals: Website visits, chat conversations, form fills
- Second-party signals: Social engagement, community participation
- Third-party signals: Research intent (Bombora), firmographic data (Clearbit, ZoomInfo)
- CRM data: Deals, activities, historical relationships
- Enrichment data: Contact information, job changes, company news
The context graph's job is to unify these sources through identity resolution and present a coherent entity model.
How do context graphs improve AI agent performance?
Context graphs improve AI performance in three ways:
- Reduced hallucination: Agents have access to real entity relationships instead of guessing
- Better decisions: Computed columns pre-digest complex data into meaningful signals
- Continuous learning: The ledger layer enables feedback loops that improve policies over time
What is the ledger layer and why does it matter?
The ledger layer is an immutable log of every decision made by the GTM system. Each decision record includes:
- What decision was made
- What context existed at decision time
- What policy version was active
- What outcome resulted (filled in later)
This matters because it enables: auditability (why did the AI do that?), debugging (what went wrong?), and learning (what works?).
How do you handle identity resolution in a context graph?
Identity resolution is the process of determining whether records across different systems refer to the same entity. Modern context graphs use multi-vendor consensus:
- Query multiple data providers for the same entity
- Compare returned data across providers
- Accept matches where 2+ providers agree
- Flag conflicts for human review This approach achieves ~90% accuracy while identifying edge cases that need attention.
Can I use a context graph with my existing CRM?
Yes. Context graphs integrate with Salesforce, HubSpot, and other CRMs bidirectionally. The CRM remains your system of record for deals and activities, while the context graph provides the unified entity model and AI reasoning layer. Data flows both ways—CRM updates feed the graph, and graph-driven actions update the CRM.
What's the difference between a context graph and a knowledge graph?
Knowledge graphs typically represent static facts and relationships (like Wikipedia's structured data). Context graphs are designed for dynamic, time-series data with a focus on decision-making:
- Context graphs include temporal information (when things happened)
- Context graphs have a ledger layer for decision logging
- Context graphs have computed columns optimized for AI consumption
- Context graphs are built for real-time queries, not just knowledge retrieval
How do policies work in a context graph architecture?
Policies are versioned rules that govern how AI agents behave. They sit between raw AI capabilities and production execution, encoding:
- Business logic (ICP definitions, routing rules)
- Compliance requirements (outreach limits, opt-out handling)
- Learned preferences (channel selection, timing) Policies evolve based on outcomes - when the ledger shows something isn't working, you update the policy and track the version change.
What infrastructure do I need to build a context graph?
Minimum infrastructure:
- Graph database or relational DB with good join performance
- Event streaming (Kafka, etc.) for real-time updates
- API layer for agent interactions
- Storage for ledger (append-only, high durability)
You can start simple with PostgreSQL and add specialized infrastructure as you scale.
How much does it cost to build a context graph?
The honest answer: it depends on your approach. DIY build (4 weeks):
- Engineering time: ~60-85 hours of data engineering work
- Infrastructure: $200-500/month for databases, streaming, storage
- Data vendors: $5K-50K/year depending on enrichment needs
- Ongoing maintenance: ~5-10 hours/month
Buy vs. build tradeoffs:
- Building gives you full control but requires dedicated data engineering
- Buying from a vendor (like Warmly) gets you to value faster but less customization
- Hybrid approach: use vendor for identity resolution, build your own ledger layer
Most teams that build internally already have data engineers on staff. If you're hiring specifically for this, factor in 1-2 full-time equivalent effort for the first year.
What is a decision trace and why does it matter for sales?
A decision trace captures the full reasoning chain behind every GTM decision: what inputs were gathered, what policies applied, what exceptions were granted, and why. As Arize AI notes, "agent traces are not ephemeral telemetry - they're durable business artifacts." For sales, this means:
- Knowing why an account was prioritized (or deprioritized)
- Understanding which signals triggered outreach
- Auditing why a specific message was sent
- Learning from outcomes to improve future decisions
How is a context graph different from a semantic layer?
A semantic layer defines what metrics mean (revenue = X + Y - Z). A context graph captures how decisions get made using those metrics. As the Graphlit team explains, you need both: operational context (identity resolution, relationships, temporal state) and analytical context (metric definitions, calculations). Context graphs extend semantic layers by adding:
- Decision logging (why was this number used?)
- Temporal qualifiers (what was the value at decision time?)
- Precedent links (what similar decisions were made before?)
Who owns the context graph - vendor or enterprise?
This is an active debate in the industry. As Metadata Weekly discusses, enterprises learned from cloud data warehouses that handing over strategic assets creates vendor leverage. For GTM context graphs specifically:
- Decision traces are yours - The reasoning connecting your data to actions is enterprise IP
- Entity models can be shared - Company/person matching benefits from vendor scale
- Policies must be enterprise-controlled - Your business rules define your competitive advantage
Look for vendors that let you export decision traces and don't lock you into proprietary formats.
What's the difference between context graphs and RAG (Retrieval-Augmented Generation)?
RAG retrieves relevant text chunks to augment LLM prompts. Context graphs go further by modeling entity relationships and decision traces.
| Aspect | RAG | Context Graph |
|---|
| Returns | Text chunks | Structured entities + relationships |
| Understands | Text similarity | Entity identity across systems |
| Logs | Nothing | Every decision with context |
| Learns | Doesn't | Feedback loops improve policies |
You can use RAG within a context graph - for example, to retrieve relevant case studies when crafting outreach. But the graph provides the structure that makes RAG outputs actionable.
How do context graphs handle real-time vs. batch data?
Context graphs support both through a tiered approach, as Merge describes:
- Live API data - Real-time queries for current state (is this person still employed here?)
- Cached data - Recent snapshots for speed (last 30 days of activity)
- Derived summaries - Computed aggregates for AI efficiency (engagement score, buying stage)
The key is balancing freshness against latency. Intent signals need real-time; firmographic data can be cached.
Context Graphs Enable Long Horizon Agents
Everything we've described - unified entities, decision ledgers, computed columns - culminates in one capability: long horizon agents.
Long horizon agents are AI systems that complete complex, multi-step tasks spanning hours, days, or weeks. They're the opposite of the "AI SDRs" that send a sequence and forget. They remember. They learn. They improve.
Why context graphs are the foundation: Without a context graph, long horizon agents are impossible:
- No entity memory → Agent can't remember talking to Sarah 3 weeks ago
- No relationship awareness → Agent doesn't know Sarah is the champion on an active deal
- No decision traces → Agent can't learn from what worked (or didn't)
- No computed context → Agent burns tokens on raw data instead of meaning
With a context graph, agents can:
- Track that John visited pricing 3 times, his boss Sarah is the CRO, and they lost a deal 6 months ago to "timing"
- Coordinate outreach across the buying committee over weeks
- Remember objections from previous conversations
- Learn that re-engaging closed-lost accounts after leadership changes works
The technical enablement: The agent harness provides the coordination and policy infrastructure. The context graph provides the world model the harness operates on. Together, they enable the "agentic loop" that defines long horizon agents:
| Capability | What Context Graph Provides |
|---|
| Perceive | Unified entity view across all signals |
| Think | Computed columns with meaning, not noise |
| Act | Decision API with full context |
| Reflect | Ledger layer connecting decisions to outcomes |
According to METR research, AI agent task completion capability is doubling every ~7 months. The companies building context graphs now will have the infrastructure for the next generation of autonomous GTM.
Conclusion: Context Graphs Are GTM Infrastructure for the AI Era
The shift from "AI as a feature" to "AI as the operator" requires a fundamental rethinking of GTM data infrastructure.
Traditional tools give you signals. Context graphs give you meaning.
Traditional tools execute actions. Context graphs execute decisions and remember why.
Traditional tools measure activity. Context graphs close the loop from decision to outcome to learning.
Is It Worth the Investment?
Honestly? It depends on your stage and resources.
If you have:
- SDR/AE teams doing manual research and routing
- Multiple disconnected data sources (CRM, intent, enrichment)
- Plans to use AI agents for GTM automation
- Data engineering capacity or budget Then yes - context graphs will pay off. Teams report 40-60 minutes saved daily per rep, 20%+ pipeline capacity improvements, and the ability to scale outbound without scaling headcount.
If you don't have:
- Dedicated data engineering resources
- An outbound sales motion
- Multiple data sources to unify
You might be better off starting with simpler intent tools and revisiting context graphs when you scale.
If you're building AI agents for GTM - whether for inbound, outbound, or marketing ops - the context graph is your foundation. It's the world model that enables AI to reason about your business instead of just pattern-matching on disconnected data.
Next steps:
- DIY path: Start with Week 1 of our implementation guide above. PostgreSQL + the core entity model gets you surprisingly far.
- See it in action: Book a demo to see how Warmly's AI agents operate on context graph infrastructure.
- Go deeper: Explore our AI Signal Agent to see unified entity resolution in practice.
Context Graph Tools and Vendors (2026)
The context graph space is evolving rapidly. Here's a landscape view:
| Category | Vendors | GTM Focus |
|---|
| GTM-Specific Context Graphs | Warmly, Writer | ✅ Built for revenue teams |
| General Enterprise | Atlan, Graphlit, Fluency | Broad enterprise, not GTM-specific |
| Intent Data + Orchestration | [6sense](/p/comparison/vs-6sense), [Demandbase](/p/comparison/warmly-vs-demandbase) | Signals without decision traces |
| Graph Databases | Neo4j, TrustGraph | Infrastructure, not applications |
| Data Platforms | Snowflake, Databricks | Warehouse, not context graph |
| Agent Infrastructure | AWS AgentCore, LangChain | Agent tooling, no GTM entity model |
|
Key evaluation criteria: 1. Does it model GTM entities (Company, Person, Employment, Deal)?
2. Does it log decisions with context snapshots?
3. Does it support computed columns for AI efficiency?
4. Does it integrate with your CRM bidirectionally?
5. Can you export your decision traces?
Further Reading
The AI Infrastructure Trilogy
From Warmly
External Resources
Last updated: January 2026