Agent v1
Baseline
62%
Accuracy on unseen held-out cases
Observed gap
Misses relationships across case history.
ENTERPRISE MULTI-AGENT PLATFORM
AI that understands how your operation works.
Connect AI agents to your enterprise systems, coordinate specialist work, and route every consequential action through human review.
Operational objective
- RetrieveFind the relevant records and source material.
- ReasonCompare evidence against the business context.
- ValidateCheck the proposed result before action.
- ActPrepare the next approved step.
Recommended next action
Your business already has the answers. They’re just scattered everywhere.
Decisions depend on systems, documents, people and history that were never designed to work as one.
Core systems
Transactions and state
Documents
Policies and procedures
People
Decisions and expertise
History
Cases and outcomes
Live signals
Events and anomalies
Conversations
Unrecorded context
ONE QUESTION
Where is the full context?
AI cannot act intelligently until that context comes together.
Turn scattered context into coordinated intelligence.
Agent Hive builds a living case around the objective, links every piece of evidence and keeps specialist agents aligned through review and action.
A living case is the shared file every agent and reviewer works from, updated as evidence comes in.
WORKING OBJECTIVE
Resolve the issue and recommend the safest next step.
Systems
Operational state
Documents
Rules and procedures
People
Expert judgment
History
Cases and outcomes
Signals
Events and anomalies
Shared operational context
Objective, evidence, relationships and memory
- Entities linked
- Constraints applied
- History remembered
- DA
Data agent
Retrieves relevant records and live changes.
EVIDENCE LINKED
- KA
Knowledge agent
Applies policy, procedures and past precedent.
CONSTRAINT APPLIED
- AA
Analysis agent
Tests competing causes against the full case.
HYPOTHESIS RANKED
EVIDENCE
Relevant change found
CONSTRAINT
Operating rule identified
PATTERN
Past case matched
REASONED CASE STATE
Evidence aligned around one next step
HUMAN REVIEW
Inspect the case
Sources, reasoning and confidence included
ACTION
Approve the next step
Traceable, controlled and ready to execute
A COORDINATED INVESTIGATION
Complex problems rarely live in one system. Neither should the investigation.
Watch one operational issue move from scattered signals to a reviewed next step.
OPERATIONAL ISSUE
Why is a critical process repeatedly failing?
SIGNAL RECEIVED
An unusual pattern opens an investigation.
CONTEXT GATHERED
Relevant records, policies, history and changes join the case.
- Records
- Policy
- History
- Changes
SPECIALISTS INVESTIGATE
Three agents test the evidence from different angles.
- DATA
- KNOWLEDGE
- ANALYSIS
EVIDENCE CONVERGES
A recent change, repeated anomaly and related case point to one cause.
LIKELY CAUSE
A recent process change conflicts with an existing operating constraint.
HUMAN REVIEW
Evidence checked
Different industries. The same coordination challenge.
Agent Hive adapts to the systems, language and controls of each environment, while keeping evidence and human oversight at the center. The depth of published proof varies by domain.
Manufacturing
Bring plant data, procedures and engineering judgement together before anyone decides what happens next.
Automotive
Connect field evidence, engineering context and human review across the vehicle case.
FMCG
Bring order, stock, allocation and delivery evidence together so teams can explain what happened.
AGENT HIVE
One governedcoordination layer
- Objective
- Context
- Specialists
- Review
- Action
Adapts to domain context
Healthcare
Bring claims, clinical data, quality measures and policy into one clear investigation.
Telecom
Bring the request, live network state, vendor rules and service data together before anyone makes a change.
Semiconductor
Bring lot history, tool data and engineering knowledge into one investigation before deciding what happens next.
The domain changes. The operating pattern stays coordinated, traceable and governed.
Built for work where the path isn’t predefined.
Connect context, coordinate agents, carry knowledge across tasks and keep execution visible in one operating layer.
Connect context
Bring the right context into the work.
Agents start with relevant operational context from the systems, data and knowledge your teams already use.
Connected at the source
Use live systems without copying work into a separate tool.
Relevant to the objective
Retrieve what matters for the case, not everything available.
Permission aware
Respect the access rules already attached to enterprise information.
Systems, APIs, documents, databases and tools converge into shared operational context.
- Systems
- APIs
- Documents
- Databases
- Tools
Shared context
Objective-ready
Case state
Relevant context assembled
Sources remain traceable
Orchestrate specialists
Coordinate agents around one objective.
Give each specialist a focused role while every finding remains connected to the same evolving case.
One shared objective
Keep specialist work aligned to the outcome that matters.
Evidence converges
Combine distinct findings instead of producing isolated answers.
Humans stay informed
Make ownership, reasoning and proposed next steps visible.
A shared objective branches to data, knowledge and analysis agents before evidence converges.
Shared objective
Resolve the recurring issue
- DA
Data
Recent change found
- KA
Knowledge
Constraint applied
- AA
Analysis
Pattern matched
Coordinated case
Evidence aligned around one cause
Remember outcomes
Carry knowledge across tasks and decisions.
Turn completed work into reusable organizational memory, so every new case begins with more understanding.
Case history retained
Preserve the evidence and reasoning behind prior decisions.
Feedback becomes context
Use reviewer guidance to improve later investigations.
Knowledge compounds
Connect related outcomes instead of starting from zero.
Case history, decisions, outcomes and feedback form a reusable organizational memory.
Operational memory
Context that compounds
- Case history
- Decision
- Outcome
- Feedback
Next investigation
Starts with relevant precedent
Operate with control
Keep execution visible and accountable.
Move from recommendation to action with policies, observability and human approval built into the operating path.
Controls travel with the work
Apply access and deployment policies throughout the case.
Execution stays visible
Observe agent activity, evidence and decisions in one place.
Approval is explicit
Require accountable review before consequential action.
A recommendation moves through policy checks, observability and human approval before controlled action.
Governed operating boundary
Recommendation
Next step proposed
- Policy checked
- Activity observed
Human approval
Sources and reasoning reviewed
Controlled action
Ready to execute
One objective. One shared case. One governed outcome.
Explore the PlatformDeployment shouldn’t be the finish line.
Describe the improvement, generate practice, train the model and prove the next version on unseen cases before release.
- DescribeSet the improvement objective
- PracticeCreate representative cases
- TrainImprove the underlying model
- ProveCompare on the same held-out set
- DeployRelease the stronger version
Improvement objective
Connect current evidence to relevant case history.
Illustrative evaluation
Gym
Improvement loop
- PracticeRepresentative cases
- TrainFeedback updates the model
- ProveSame held-out cases
Agent v2
Proven
89%
Accuracy on unseen held-out cases
+27 points
Connects current evidence to relevant case history.
Ready for governed release
- Same evaluation setfor both versions
- Results visiblebefore deployment
- Repeatable loopfor ongoing improvement
This trains the model, not another prompt.
Gym turns a defined improvement goal into practice, training and measurable evidence.
One objective in. A better-trained agent out.
Only the version that proves improvement is prepared for governed deployment.
See how Gym worksGive agents autonomy without giving up control.
Define how work is evaluated, observed, reviewed and approved before it moves into operational use.
Explore trust and governanceEvaluate
Test before agents act.
Evaluation evidence attached
Observe
See how every result was formed.
Full execution trace available
Review
Keep people at consequential points.
Human decision required
Deploy
Move only approved agents into work.
Controlled release path
Bring us one complex workflow.
We’ll map the systems, knowledge, handoffs and controls behind it, then show how Agent Hive could work across them.