Context-Driven Agents

Agents are only as good as the context they act on.

Most agent projects fail on context, not models. We build the layer that gives agents a reliable, permissioned view of your business: CRM objects, call transcripts, emails, product docs, pricing, playbooks and intent signals. We then expose it to your agents and your team’s AI tools through MCP.

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Quick answer

What is a context layer for AI agents?

A context layer is a permissioned data layer that gives AI agents one reliable view of your business: CRM records, call transcripts, emails, documents, pricing and buying signals. It handles retrieval, memory and access control, and exposes tools through the Model Context Protocol (MCP) so any compatible agent or AI assistant can use them safely.

Use cases

What Context-Driven Agents handle

01

Unified account context

One retrieval layer across CRM, call recordings, email, tickets and product usage.

02

Playbook memory

Your messaging, objection handling, pricing rules and ICP, versioned and kept in one place.

03

MCP servers for your stack

Give Claude, ChatGPT, Copilot or Agentforce safe, scoped access to your systems.

04

Multi-agent orchestration

Coordinate research, outreach, CRM and voice agents so they share one view of each account.

05

Evaluation & observability

Test sets, traces and scorecards for every agent so quality goes up with each release.

How we build it

From your playbook to a production agent.

  1. Inventory

    Map where context lives today and who owns it.

  2. Model

    Define the entities, relationships and permissions your agents need.

  3. Pipe

    Sync and index sources with freshness rules and access controls.

  4. Serve

    Expose context to agents and tools through retrieval and MCP, with full tracing.

Capabilities

What it can do

  • Hybrid search and retrieval over structured and unstructured data
  • Row- and field-level permissions inherited from source systems
  • MCP server development and hosting
  • Agent memory and session state
  • Tracing, evals and regression tests
  • Model-agnostic design that works with Claude, GPT, Gemini and open models

Guardrails

What keeps it safe

  • Source-system permissions enforced at query time
  • PII handling and data-residency options
  • Deploy in your cloud account if required
  • Prompt-injection defences on external content

Works with

  • Salesforce Data 360
  • Snowflake
  • BigQuery
  • Postgres
  • Gong
  • Google Drive
  • Notion
  • Confluence

Measured on outcomes

The numbers we report every month.

We agree on baselines before launch and track them on a shared dashboard, so you can see the agent’s impact, not just its activity.

  • Answer accuracy on eval sets
  • Context freshness
  • Agent task success rate
  • Latency
  • Cost per task

Pairs well with

Agents that share the same context

Voice Agents

Inbound and outbound calling agents that qualify, book, follow up and hand off warm to your reps.

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Chat Agents

Website, in-app and messaging agents that answer, qualify and book meetings while buyers are still on the page.

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Email Agents

Agents that work your inboxes: reply handling, signal-triggered outreach, follow-ups and shared-inbox triage.

Explore

Guides

Further reading

6 min read

What is Agents as a Service? A guide for revenue teams

How Agents as a Service works for revenue teams, what it includes, how it’s priced, and how it compares to buying an AI tool or building in-house.

Read the guide 5 min read

Agentforce vs. custom AI agents for Salesforce: how to choose

Agentforce or a custom agent on Salesforce’s APIs? A practical comparison of data, channels, model choice, governance and time to value.

Read the guide 6 min read

What is a context layer for AI agents, and why MCP matters

Why AI agents fail without context, what a context layer contains, and how MCP lets AI assistants use your CRM and data safely.

Read the guide

FAQ

Questions, answered

Something else? Ask us directly.

What is MCP and why does it matter?

The Model Context Protocol is an open standard that lets AI assistants and agents use tools and data in a consistent way. An MCP server for your CRM or data warehouse means any compatible AI tool can work with it safely, within the permissions you set.

Do we need a data warehouse first?

No. We start with the systems you have and add a warehouse or vector store only when the use case needs one.

Next step

See Context-Driven Agents working on your workflow.

Book a 30-minute call. We’ll map your highest-leverage use case, show a working agent and tell you honestly what it would take.

Book a demo