For Agent Builders & Orchestration Engineers
Auras for Agent Task Orchestration
Embed a persistent personality layer into your AI agents — so tone, context, and decision-making stay coherent across every tool call and multi-step workflow.
What is an Aura?
An Aura is a psychologically grounded personality layer — built on Big Five traits and CliftonStrengths® — that an AI agent carries across every tool call and step, keeping its tone, context, and decision-making consistent. 34Five builds Auras so multi-tool agents stay coherent, predictable, and trustworthy to the people who rely on them.
The Multi-Tool Agent Coordination Challenge
Modern agents orchestrate across email, CRM, knowledge bases, task managers, and custom APIs. Without a stable behavioral layer, coherence breaks down across tool calls.
Context lost between tool calls
Each tool starts cold. State from the prior step doesn't carry forward, so the agent re-derives or forgets.
Inconsistent tone & decisions
The same agent sounds warm in an email and clinical in a CRM note — confusing humans and downstream systems.
Outputs that confuse systems
Disjointed outputs across tools break the assumptions of the next step in the pipeline.
Unpredictable behavior
When an agent acts differently each run, users stop trusting it — the fastest way to kill adoption.
Variance-driven errors
Unanchored behavior means the same prompt can drift tool-to-tool, amplifying hallucination.
Memory for what Matters
Reducing the frustration for loss of memory. Active and continuous listening to ensure the task gets done right, and if not to instruct the agent workforce to make the appropriate corrections.
Auras: A Persistent Behavioral Layer for Agents
An Aura is a psychologically grounded personality profile (Big Five + CliftonStrengths) your agent carries into every tool interaction — anchoring context, tone, and decision-making.
Context persistence
Auras retain semantic understanding of prior steps, so calling Tool 2 remembers what happened in Tool 1.
Context coherence 8.2 / 10 vs. 3.4 baseline*
Consistent personality
Every interaction reflects the same tone and decision-making — the email matches the CRM note matches the task.
Output consistency 94% across tool combos*
Reduced hallucination
Anchoring behavior to stable psychological traits narrows output variance — fewer drift-driven errors.
False-positive rate 2.1% vs. 8.7% baseline*
Human trust
A consistent personality gives people a mental model of the agent — predictability is what earns trust.
User trust 7.6 / 10 vs. 4.2 baseline*
*Illustrative figures shown for explanatory purposes, not measured benchmark results.
Integrate Auras Into Your Agent Framework
Auras are designed to drop into modern agent stacks. The SDK and hosted API are launching soon — early-access partners get first integration support.
LangChain Agents
Add an Aura as middleware so every tool call runs through the personality + context layer.
~2 lines to add · Coming soon
CrewAI
Assign an Aura per crew member so multi-agent collaboration stays coherent and in-character.
Per-agent config · Coming soon
OpenAI Assistants
Use an Aura as the system persona so personality persists across the full conversation history.
System persona · Coming soon
AutoGen
Configure an Aura for each agent role to keep multi-agent conversations consistent.
Per-role config · Coming soon
REST API
Framework-agnostic. Call the Aura API directly from any custom orchestration layer.
HTTP request/response · Coming soon
Webhooks
Event-driven updates to drive reactive, personality-aware agent systems.
Event mapping · Coming soon
Agent Performance With Auras
Auras are built to improve reliability, consistency, and user trust across multi-tool workflows. The figures below are illustrative of the outcomes we design for.
Task Completion
94%
End-to-end without re-routing (vs. 71% baseline).
Context Coherence
8.2
Across 10+ sequential tool calls (vs. 3.4 baseline).
Output Consistency
94%
Same prompt across 10 tool combinations.
User Trust
8.6
Rated reliable and predictable (vs. 4.2 baseline).
Illustrative figures shown for explanatory purposes — not measured benchmark results.
Illustrative Case Study
BDR Agent: Coherent Email + CRM + Tasks
A BDR agent sent personalized emails but produced inconsistent CRM notes and misaligned follow-up tasks. Adding an Aura unified the personality across all three tools — warm in email, organized in the CRM, methodical in task management.
Modeled outcomes: email response 8.2% → 12.4% · CRM data quality 62% → 88% · sales-team satisfaction 4.1 → 7.3 · cleanup time 2.1 → 0.4 hrs/day.
Choose Your Path
Agent Builders
Building autonomous agents — BDR, customer success, research, content. You want integration examples, performance data, and a fast quickstart.
Platform Engineers
Running multi-agent orchestration and frameworks. You want scalability specs, latency data, deployment patterns, and SLAs.
Framework Contributors
Building open-source agent frameworks. You want the integration spec, API contracts, webhook patterns, and SDK details.
Getting Started With Auras in 3 Steps
Step 1 · 4-6 hrs
Define your Aura
Use the Aura interview to capture Big Five traits, CliftonStrengths, and role context — producing a personality profile.
Step 2 · ~10 min
Add it to your agent
A few lines of code wrap your agent so the Aura carries context and personality through every tool call.
Step 3 · ~20 min
Deploy & monitor
Ship to production and track personality adherence and context coherence from the Aura console.
Preview — Python SDK launching soon
from aura import Aura
# 1 · Load the profile from the interview
aura = Aura.from_json("my_aura.json")
# 2 · Wrap your existing agent
agent = with_aura(my_agent, aura)
# 3 · Run — the Aura handles every tool call
agent.invoke({"input": "Your task here"})
Frequently Asked Questions
How do Auras maintain context across tools?
The Aura layer retains semantic context from each tool interaction in a memory-augmented store, so state persists across many sequential calls without bloating the prompt.
Do Auras work with my existing agents?
They're designed to drop in as middleware or a system persona with minimal code changes. The SDK and hosted API are launching soon — early-access partners get first support.
What's the latency impact?
We design for a small per-call overhead — on the order of a few hundred milliseconds — negligible for most agent workflows, with on-prem options for latency-critical deployments.
How does personality affect reliability?
Anchoring behavior to stable psychological traits narrows output variance, which reduces drift-driven errors as a task moves across tools.
Can I create custom Aura personalities?
Yes — through the structured Aura interview (Big Five + CliftonStrengths + role context) or a direct profile configuration for advanced users.
What data does an Aura need?
At minimum, Big Five trait scores plus role context. Optimally, also CliftonStrengths and conversation history — Auras improve as they process more interactions.
How much does it cost?
Pricing will be usage-based with a free tier for pilots and POCs. Reach out for early-access details.
Do Auras work with reasoning models?
Yes. An Aura layers on top of your reasoning model — the model reasons, the Aura keeps the output consistent in tone and personality.
Ready to Build Better Agents With Auras?
Ensure your intake layer has embed personality and emotional intelligence to ensure the message gets properly communicated into your agent orchestration. Join the early-access program and get first integration support.
Start building
Get into the integration guide and code examples as they launch.