How Aurora Agents Share Context Across Your AI Team

How shared context works across Aurora Agents

Aurora Agents work inside the same authorized Finta workspace, so a research teammate, an outreach teammate, and a follow-through teammate can use the relationship and deal context their jobs require. They are not three disconnected chatbots that must be briefed from zero.

They are also not one indistinguishable mind. Each teammate has its own identity, instructions, persistent conversation, owned routines, and work history. Skills, connected Apps, and memory remain workspace-level resources shared with Aurora and every teammate. Those layers organize responsibility and continuity, while the workspace's actual access controls determine what data and actions are available.

The practical model is:

shared authorized workspace + distinct teammate responsibility

Shared context reduces repeated explanation. Distinct ownership makes it clear who is doing what, where its work returns, and which instructions should change when the result misses the mark.

Shared does not mean copied into every prompt

Teams sometimes hear "shared context" and imagine one enormous prompt containing every email, contact, document, meeting, and prior conversation. That would be neither precise nor reliable.

Context is a selection problem. A teammate should retrieve the smallest useful set of permitted information for the current responsibility, not everything that happens to be available.

Anthropic's context-engineering guidance describes context as finite and argues for high-signal information, clear instructions, focused tool sets, and deliberate retrieval. Google's Agent Development Kit architecture similarly separates sessions, memory, and artifacts as different sources of structured state.

Those are general engineering principles, not evidence about Finta's internal implementation. They explain why useful multi-agent work needs more than a shared transcript.

The five-layer teammate context map

Use this map to understand which layer answers which question.

The distinction prevents two common failures. First, every teammate becoming a generic assistant with a different name. Second, teams assuming that a separate avatar creates a separate confidentiality boundary.

A three-teammate example for an emerging fund manager

Suppose a GP has three recurring responsibilities during a fundraise.

The research teammate

Its job is to keep a focused set of potential LPs current. It uses the mandate criteria in its instructions, relevant workspace records, available research tools, and source-linked findings. It returns a brief in its own conversation.

An output might say:

I found two changes in the current prospect set. One institution now describes a different geographic focus. The official source is dated September 18. I left the fit decision unchanged for your review.

The teammate has retrieved shared workspace context, but the assessment remains attached to a recognizable owner and evidence.

The communication teammate

Its job is to prepare relevant follow-up from the approved relationship history and the GP's voice preferences. It can use a research brief when that information is permitted and useful. It does not inherit authority to contact the LP merely because another teammate found the prospect.

Its output might say:

I prepared the follow-up based on the LP's stated question and the approved fund materials. The research note was not needed. Nothing has been sent.

This is a useful non-handoff. Shared context does not mean every fact must enter every task.

The follow-through teammate

Its job is to preserve open commitments, owners, and dates. It may turn an approved meeting outcome into a reviewable next-step list and maintain a weekly routine.

Its output might say:

The data-room follow-up is assigned to Morgan for Thursday. The legal question has no owner. I left it blocked rather than assigning it automatically.

Each teammate benefits from the same authorized workspace. Each still has a different job, output, and point of review.

What should travel between teammates?

Useful handoffs transfer a compact, inspectable artifact rather than an entire conversation.

A useful handoff includes:

• the objective;
• the source-backed facts needed for the next job;
• the current decision or approval state;
• unresolved questions;
• references to the relevant people, records, or documents;
• the expected output;
• and the authority boundary.

For example:

Prepare a meeting brief for Northline. Use the current CRM relationship, the September 24 email thread, and the sourced mandate update from the research teammate. Do not draft outreach. Flag any conflict between the mandate note and the CRM record.

That handoff is more reliable than "Continue what Researcher was doing." It explains what should persist and what must be checked again.

What should remain separate?

Separation matters when the responsibility, audience, source policy, or decision changes.

Keep these items distinct:

• Standing instructions. A research standard should not silently become a communication standard.
• Conversation history. A teammate's thread should preserve its own assignments and corrections.
• Role instructions. Preferred brief length may belong in a research teammate's instructions. Recipient-specific consent belongs in the governed relationship record, not an isolated role note.
• Routine ownership. A weekly prospect review and a daily follow-up check should have named owners and independent pause controls.
• Approval state. Approval of research does not approve a message, introduction, document share, or investment decision.
• Client or mandate boundaries. Separate teammates do not establish legal or technical isolation between clients, funds, families, or transactions. Use the underlying workspace and data-governance controls required for that separation.

Shared context is not shared certainty

Several teammates can repeat the same error if they all rely on one stale or unsupported source. Shared context can accelerate good work and propagate bad assumptions.

Use four checks before a teammate treats context as durable:

1. Provenance: Where did the claim come from?
2. Freshness: When was it observed, and could it have changed?
3. Status: Is it a verified fact, a user instruction, an agent inference, or an unresolved conflict?
4. Correction: Where can a person amend or remove it?

The AI Agent Memory for Relationship Workflows guide provides a deeper retain, retrieve, verify, and forget framework. This article addresses the different question of how several recognizable teammates use context without losing role clarity.

How Aurora Agents preserve identity

Aurora remains Finta's primary agent. Optional teammates organize specific responsibilities under that relationship.

The identity is useful because it gives the work a stable home:

• instructions describe the continuing job;
• shared workspace Skills, connected Apps, and memory provide authorized resources;
• a persistent conversation receives assignments and results;
• owned routines return recurring work to the same teammate;
• and run history helps a person inspect what happened.

These features make responsibility recognizable. They do not create a separate model, organization, or permission silo. The teammate still operates inside the user's authorized Finta environment, subject to the sources, tools, and controls available there.

Questions to ask about any multi-agent product

When a platform says its agents share context, ask:

1. Which context is workspace-level, instruction-specific, conversation-specific, or task-specific?
2. Who controls the sources each agent may retrieve?
3. Are tools selected for relevance, permission, or both?
4. Does each teammate have a persistent place to report back?
5. Can users inspect and correct durable memory?
6. Can one agent see another agent's raw conversation, or only an explicit handoff?
7. What happens when two teammates produce conflicting conclusions?
8. Does creating another agent change access rights?
9. How are routines paused, reassigned, or audited?
10. Which external actions still require review?

The answers matter more than the number of agents shown in a sidebar.

Build an AI team around responsibilities, not personalities

Personification helps people recognize a teammate and remember its job. It should not hide the operating contract.

Start with a responsibility that deserves continuity. Write the instructions. Confirm which shared workspace Skills, connected Apps, and memory are appropriate for that responsibility. Decide what belongs in its conversation. Assign recurring work only after the output and review point are clear.

Then ask a simple question after each run: did the teammate return the right work, with the right context, to the right place?

Use AI Agent Routines: Give Recurring Work a Teammate to add cadence. Use the AI Teammates for Private Capital guide to map responsibilities across founders, fund managers, family offices, and real estate teams.

Explore Aurora to see how Finta carries relationship and deal context into reviewable work.

Sources and disclosure

• Finta Aurora
• Finta Automations
• Anthropic: Effective Context Engineering for AI Agents
• Google Developers: Architecting an Efficient Context-Aware Multi-Agent Framework
• NIST: AI Agent Identity and Authorization Concept Paper

Research updated September 27, 2026. Written by Finta Editorial Team and reviewed by Finta Product and Editorial. The context map is an editorial framework. It is not a security architecture, data-retention commitment, or guarantee that a particular source is available in every Finta workspace. Product behavior depends on the workspace, permissions, connections, enabled capabilities, and current release. This article is general operational education, not investment, legal, privacy, security, or compliance advice.