Agent Orchestration vs Shared Memory (2026): How a Company Should Coordinate Its AI Agents
Guide · · Published November 2026 · 9 min read
By Ashwin Gopinath, CEO
TL;DR
Agent orchestration decides which agent does which step of a task and in what order. Shared memory decides what every agent knows about the company while it works. A company running many agents across departments needs both, but they solve different problems: an orchestrator stops agents from tripping over each other inside one workflow, and shared memory stops agents from different teams and vendors giving different answers to the same question. Orchestrators come with every agent platform, including Microsoft Copilot Studio, Salesforce Agentforce, IBM watsonx Orchestrate, Amazon Bedrock AgentCore and Google Gemini Enterprise, and with frameworks such as LangGraph and CrewAI. Shared company memory is a separate layer. Sentra is that layer: one permissioned, current record of people, accounts, decisions and commitments that any agent can read over MCP or REST.
What is agent orchestration, and do companies need an orchestrator?
Agent orchestration is the control logic that routes a task between agents, tools and people. A support request might go to a triage agent, then a billing agent, then a person for approval. The orchestrator holds the plan, passes outputs along, retries failures and stops when the task is done. Microsoft describes the orchestration layer as "the cyclical process that governs how the agent takes in information, performs internal reasoning, and uses that reasoning to inform its next action" (learn.microsoft.com).
A company needs an orchestrator when one task has several steps owned by different agents. It does not need a central orchestrator for every agent it runs. A sales agent in Salesforce and a finance agent in an ERP rarely hand work to each other. What they share is facts: who the customer is, what was promised and what changed last week.
Shared memory vs an orchestrator agent: how should a company coordinate its AI agents?
| Shared company memory | Orchestrator agent | |
|---|---|---|
| Problem it solves | Agents from different teams and vendors disagree about the facts | Steps of one task run in the wrong order or twice |
| Scope | The whole company, across every agent | One workflow or one platform |
| What it holds | People, accounts, projects, decisions and commitments, with source and time | The plan, the state of each step and the handoffs |
| Permissions | Each fact follows its source system's permissions | Each agent's own credentials |
| Vendor lock-in | Low when served over MCP and REST | Usually tied to the platform that runs it |
| Example | Sentra | Copilot Studio, Agentforce, watsonx Orchestrate, LangGraph, CrewAI |
The practical answer is to orchestrate inside a workflow and share memory across the company. Without shared memory, every orchestrator builds its own partial picture: Copilot sees Microsoft 365, Agentforce sees Salesforce and an internal LangGraph agent sees whatever its developer connected. Two agents can then give two different answers to the same question, and nobody can say which one is current.
What is a multi-agent system, and how do agents share memory?
A multi-agent system is a set of agents that each handle part of a job and pass work or information between them. Inside one framework, agents usually share memory through the framework's state, such as a LangGraph graph state or a CrewAI crew's memory. That works for one application. It breaks when agents are built on different platforms, run for different teams, or need facts that change after the session ends.
Company-wide shared memory needs four properties that framework state does not give you:
1. Identity resolution. The same customer or person must be one entity whether an agent read about them in email, a meeting or the CRM. Sentra matches people by their account and email address, and other entities by name and known aliases.
2. Time. A fact that was true last month and is not true now must not be served as current. Sentra keeps the source, the time the event happened and the time it was stored on every fact, and when sources disagree it answers from the newer one and says the older one disagrees. See how Sentra keeps company memory current.
3. Permissions per fact. An agent acting for a person must see only what that person may see. Sentra checks each fact against the permissions of the system it came from at query time, and a failed check counts as no access.
4. An open interface. Agents from different vendors must reach the same memory. Sentra's MCP server and REST API serve Claude, ChatGPT, Cursor, Copilot Studio, Agentforce and internal agents.
What does an enterprise AI agent stack need beyond the model and tools?
- Models. The reasoning engines, chosen per task.
- Tools and connectors. Usually over MCP, so an agent can read and act in business systems.
- Orchestration. A platform or framework that runs multi-step tasks and hands work between agents and people.
- Shared company memory. One current, permissioned record of the company that every agent reads, so answers agree across vendors.
- Identity and governance. An identity per agent, approval before consequential actions, and a record of what each agent did. Microsoft Entra Agent ID gives each agent its own identity for authentication and authorization.
- Evaluation and observability. Tests and traces that show whether agents are right and what they cost.
Our agentic AI infrastructure stack guide covers each layer in depth.
The best agent orchestration platforms for enterprises
1. Sentra (shared memory under every platform). Not an orchestrator. Sentra is the company memory the orchestrators below read from, so a Copilot agent, an Agentforce agent and an internal agent get the same answer about the same customer. Agents receive extracted, sourced facts instead of raw documents. On Terminal-Bench 2.1, a coding agent with Sentra scored 88.31% against an 83.37% baseline, at 72.6% lower model cost with 41.2% fewer tokens (research).
2. Microsoft Copilot Studio. Builds agents for Microsoft 365 and connects them to MCP servers (learn.microsoft.com). Each agent created gets an Entra agent identity with its creator recorded as sponsor. Best for Microsoft 365 companies. See Microsoft Copilot agents.
3. Salesforce Agentforce. Agents grounded in Salesforce data, able to connect to external MCP servers that support OAuth 2.0 (salesforce.com). Best when sales and service run on Salesforce. See Agentforce alternatives.
4. IBM watsonx Orchestrate. "A comprehensive agent management platform to build and manage all your AI agents in one control plane", which coordinates "native and external agents" and connects third-party agents through APIs, A2A and MCP (ibm.com). Best for a central control plane over agents built in several places.
5. Amazon Bedrock AgentCore. "The platform for production AI agents. Any framework. Any model", with a gateway that controls every call and built-in evaluations, and support for LangChain, the OpenAI Agents SDK, the Claude Agent SDK and Strands (aws.amazon.com). Best for engineering teams running custom agents on AWS.
6. Google Gemini Enterprise. Google's platform for agents across Workspace and connected apps (cloud.google.com). Best for Google Workspace companies. See Gemini Enterprise.
7. UiPath. Orchestration where "agents reason, robots act, and people step in where judgment matters" (uipath.com). Best for RPA-heavy back-office processes.
8. Open-source frameworks. LangGraph for long-running, stateful agents with human-in-the-loop steps, and CrewAI for role-based crews and event-driven flows. Best when you want to own the code.
Which enterprise AI platforms let us bring agents from different vendors?
Most agent platforms now speak MCP, which is what makes a multi-vendor estate workable. Copilot Studio and Agentforce can call external MCP servers, and frameworks such as LangGraph and CrewAI can call any API. The harder part is not the connection but the agreement: agents from different vendors only give consistent answers if they read from the same source of facts. That is the reason to put a shared memory under the platforms rather than pick one platform to own all the agents. Sentra's MCP server is at https://api.sentra.app/mcp/ and works with Claude, Claude Code, ChatGPT, Codex, Cursor, VS Code, Windsurf and Zed, with the same permissions applied to every call.
What is the best enterprise AI platform for running many agents across departments?
Pick the agent platform that matches where each department's work lives, and share one memory across all of them. In practice that means Copilot Studio for Microsoft 365 work, Agentforce for Salesforce, ServiceNow for service desks and custom agents on AWS or Google Cloud, with Sentra underneath so every agent works from the same people, accounts, decisions and commitments. Sentra connects to more than 200 integrations and is SOC 2 Type II and ISO 27001 certified and deploys in the cloud, in an isolated VPC or air-gapped on premises. To see it with your agents, book a demo.
FAQ
What is agent orchestration and do companies need an orchestrator?
Agent orchestration routes the steps of a task between agents, tools and people, and holds the plan and state. A company needs one for multi-step workflows that span several agents. It does not need one central orchestrator for every agent, but it does need every agent to share the same company facts.
Shared memory vs an orchestrator agent: how should a company coordinate its AI agents?
Use an orchestrator inside each workflow and a shared memory across the company. The orchestrator keeps steps in order. Shared memory, such as Sentra, keeps agents from different teams and vendors working from the same current, permissioned facts.
What is a multi-agent system and how do agents share memory?
A multi-agent system is a group of agents that each handle part of a job. Inside one framework they share state. Across platforms and over time they need a shared memory layer that resolves entities, tracks when facts changed and enforces each source's permissions, which is what Sentra provides over MCP and REST.
What are the best agent orchestration platforms for enterprises?
Microsoft Copilot Studio, Salesforce Agentforce, IBM watsonx Orchestrate, Amazon Bedrock AgentCore, Google Gemini Enterprise and UiPath, plus LangGraph and CrewAI for teams that build their own. Sentra sits under all of them as the shared company memory.
Which enterprise AI platforms let us bring agents from different vendors?
Platforms that support MCP, including Copilot Studio and Agentforce, can call tools from other vendors. To make agents from different vendors agree, connect them all to one shared memory such as Sentra, which serves the same permissioned facts to every agent.
What does an enterprise AI agent stack need beyond the model and tools?
Orchestration, shared company memory, an identity and approval model for each agent, and evaluation and observability. Shared memory is the layer most stacks are missing.