Ground before planning
Specialists begin with the organisation’s relevant knowledge before deciding how to approach the work.
AlphaAgent OS
The hardest parts, pioneered.
AlphaAgent OS is the proprietary operating system beneath real agentic work. We engineer and continuously advance the foundation, so your teams can focus on reinventing the business capability.
Inherited operating guarantees
AlphaAgent OS
- Apply your expertise
Institutional knowledge becomes reusable intelligence.
- Intelligence Graph
- Share what works
Move proven assets between teams and deployments.
- Organisation Library
- Carry real work
Agents compute, coordinate and work through approved systems.
- Kernel
- Flows
- Bridge
- Remember and improve
Context persists. Improvements stay deliberate.
- Memory
- Academy
- Keep control active
Decision rights and supervision remain in force.
- Authority
- Governor
- Make outcomes defensible
Sources, computation and decisions remain inspectable.
- Lens
Proprietary by design
The complete operating system.
A model and instructions are only the start. We built ten proprietary components to solve the hard operating problems around real agentic work. Your teams inherit the complete system and every advance we make to it.
Intelligence Graph
Turn documents and relationships into reusable specialist intelligence.
Organisation Library
Share versioned agents, workflows and graphs across your organisation.
Kernel
Run real computation and durable agent work.
Flows
Govern complex processes without scripting away judgement.
Bridge
Use live systems through approved, protected connections.
Memory
Carry useful context across cases, people and time.
Academy
Test alternatives and promote only what performs.
Authority
Place access, decisions and accountability with the right people.
Governor
Supervise agent behaviour while work is running.
Lens
Keep evidence, actions and decisions with every outcome.
We are reinventing the agentic operating system so you can reinvent the business capabilities that run on it.
- Intelligence Graph
Apply your expertise.
Intelligence Graphs turn your methods, policies, research and institutional knowledge into reusable specialist expertise. Give an out-of-the-box agent a specialist skill: the knowledge of what to do, how to do it and which evidence to use.
An agent. A new skill.
Make it a specialist.
The prompt sets the objective. The Intelligence Graph supplies the depth, so you do not have to teach the whole job inside every prompt.
Start with
A capable agent
Able to reason, use tools and carry work.
Add an Intelligence Graph
Your specialist skill
Methods, models, procedures and the evidence behind them.
Put to work
A specialist agent
Equipped to apply your expertise to the task.
A prompt directs.
Sets the role, objective and instructions for the task.
A knowledge base informs.
Makes relevant documents and information available to search.
An Intelligence Graph equips.
Brings methods, relationships and source evidence together so an agent can apply a specialist skill. Build that expertise once and reuse it across authorised agents.
Specialist skills in action
One agent. Specialist skills.
A motor claim needs damage assessment, policy judgement and clear communication. Follow one agent as it draws on a different Intelligence Graph for each job, carrying the claim and its findings through to a case ready for review.
ONE CLAIM. ONE AGENT.
INSURANCE CLAIM
A claim arrives.
A collision at a junction.
Illustrative example
The customer reports front-left damage. Photographs, a repair estimate and the motor policy accompany the claim.
- Accident description
- Vehicle photographs
- Repair estimate
- Customer policy
Impact assessor
Front-left collision. Retrieve the assessment method, hidden-damage checks and report requirements.
- Map contact direction against the visible damage pattern. [1]
- Check bumper mounts and headlamp supports for concealed damage. [2]
- Separate observed, inferred and unconfirmed damage in the report. [2]
Damage assessment report
- Front-left damage is consistent with the reported contact.
- The bumper and headlamp require inspection against the repair estimate.
- Concealed mounting damage remains unconfirmed. Request inspection evidence.
Policy & legal reviewer
Damage assessed; concealed damage unconfirmed. Retrieve the coverage-review method and decision checks.
- Match the loss date and incident to the operative policy wording. [1]
- Check exclusions, excess and outstanding obligations. [2]
- Escalate uncertain interpretation; keep repair scope provisional. [2]
Coverage recommendation
- The reported collision appears to fall within the accidental-damage provision.
- Confirm the applicable excess and outstanding policy checks.
- Hold the final repair scope pending inspection. Submit the recommendation for approval.
Customer communicator
Inspection pending; coverage not approved. Retrieve guidance for a clear, accurate customer update.
- Explain what was reviewed and which findings remain unconfirmed. [1]
- State the inspection needed and what happens next. [2]
- Avoid a coverage or settlement promise before approval. [2]
Draft customer response
- “We have reviewed your photographs and repair estimate.”
- “An inspection is needed to confirm whether there is damage behind the bumper.”
- “We will confirm the repair scope and policy position once those checks are complete.”
The intelligence engine
Fully managed
Intelligence Graphs are powered by AlphaAgent’s proprietary Augmented Multi-Resolution Property Graph (AMPG) mechanism, which organises knowledge and retrieves it in context. Three stages turn source material into expertise an agent can use.
01 / Embedding
Build the graph.
Add policies, procedures, research and model documentation. AlphaAgent automatically turns them into structured, contextual Intelligence Graphs, with the whole document and its detail held together.
02 / Indexing
Connect the knowledge.
Concepts, relationships and overlaps are mapped at multiple levels of detail. New documents and domains can be added without designing and maintaining a rigid knowledge model for each one.
03 / Retrieval
Apply what matters.
For each task, the system finds relevant knowledge and follows the relationships that reveal the evidence that matters. Agents receive specialist context with a traceable path back to its source.
Stay within bounds
Retrieval follows an explicit budget, keeping exploration controlled and repeatable.
Trace every source
The Thought Map records the path from the question to the source. Retrieval supplies evidence, not an authored conclusion.
Specialist analytical work
A volatility analyst draws on model documentation, research and house methods to assess a signal in its market context.
Specialist operational work
A claims investigator draws on policy wording, procedures and exceptions to assess a case against the relevant source clauses.
Build the expertise once. Give every authorised agent the skill to apply it.
Reusable expertise
Build once. Reuse expertise across every agent.
Prometheus-builtAvailable
Specialist Intelligence Graphs from Prometheus Research Labs.
AlphaAgent ships with a growing set of Intelligence Graphs for high-value financial services use cases across capital markets, insurance, banking and payments.
Customer-builtAvailable
Your own institutional knowledge.
Teams can build Intelligence Graphs from their own policies, procedures, research, model documentation, operating guidance and specialist methods.
Organisation LibraryAvailable
Share expertise across deployments.
Share a selected Intelligence Graph version to an organisation library. Authorised teams can pull a copy into their own Studio deployment.
Build an Intelligence Graph in Studio, share a selected version through the Organisation Library, and make that expertise available to authorised teams. Their local copies remain under their own deployment’s control.
Use cases
Built for two kinds of specialist agents.
For specialist work that requires deep applied knowledge.
Quant researchers, volatility specialists, FX analysts, portfolio researchers and credit analysts need more than general language understanding. They need embedded knowledge, technical methods, mathematical context and the ability to apply them correctly.
Specialist domain knowledge
Mathematical and model context
Research workflows
Expert reasoning paths
For regulated work that requires policy, guidance and control.
KYC analysts, fraud investigators, compliance reviewers, claims handlers and underwriters need to navigate huge volumes of policy, regulation, procedure and exceptions.
Policy and regulation knowledge
Procedure and control mapping
Source-grounded decisions
Traceable escalation logic
- Organisation Library
Share what works.
Create in Studio, then share a chosen version through the Organisation Library. Teams across your organisation can pull proven agents, workflows, Intelligence Graphs and other assets into their own deployments.
Organisation Library
Shared assets
Move useful work from one Studio to teams across the enterprise.
- Share a selected version
- Publish an agent, workflow, Intelligence Graph, connector or environment from Studio to the appropriate organisation library.
- Make it available to your teams
- Library contributors can share and pull; readers can pull a copy into their own Studio deployment.
- Keep each deployment in control
- A newer library version can update a local copy. Teams review and activate workflow changes in their own deployment.
Expertise and working patterns can spread without forcing every team to rebuild them.
- Kernel
- Flows
- Bridge
Carry real work.
Agents compute against real data, coordinate adaptive work and use approved systems. Substantial runs continue without step-by-step supervision.
Kernel
Runtime execution
Execute the work.
- Coordinate specialists
- Run individual agents, coordinated teams, tools and supervised specialist work through one durable runtime.
- Compute the answer
- Execute Python and shell code against the real dataset, so calculations and charts come from completed computation.
- Continue long work
- Checkpoint progress, reconnect people to the same run and notify them when work completes or needs attention.
The output comes from work that actually ran, not text that merely sounds calculated.
Flows
Governed orchestration
Keep judgement intact.
- Combine adaptive steps
- Move between agent work, document analysis, conditions, branches and human handoffs without reducing everything to a script.
- Operate reliably
- Apply schedules, retries, timeouts, approval points and structured outputs where the process requires them.
- Pass the full work
- Move complete datasets and files between authorised steps while keeping large payloads out of the model context.
The process becomes repeatable without flattening the judgement inside it.
Bridge
Approved connection
Reach approved systems.
- Connect business systems
- Connect SQL databases, REST APIs, MCP servers, AWS services and approved business tools.
- Test before activation
- Validate connection details, available tools and expected schemas before they become part of live work.
- Protect every credential
- Resolve secrets only when authorised work invokes the connection. Use short-lived AWS credentials where available.
Agents reach the systems they need without being given unrestricted access.
One request, coordinated expertise
One team. One outcome.
Complex work draws on several disciplines. A supervisor brings the right specialists together, coordinates their tasks and combines their findings into one evidenced result.
Your people set the objective and review the outcome. The team carries the investigation between them.
“What is driving this volatility?”
Supervisor coordinates
- Macro
- Volatility
- Risk
One sourced assessment
Match control to the work
Choose what stays fixed.
Decide what agents can choose as they work and what the process must determine in advance. Both approaches operate within defined authority and retain evidence of what happened.
Dynamic teams
Follow the question.
A supervisor selects specialists as the work unfolds. New evidence can change the next task, the tools used and the expertise needed.
A research desk brings together macro, volatility and risk expertise as an investment question develops.
- Set the objective
- Adapt the team
- Review the evidence
Governed workflows
Define the operating process.
Set the sequence, decision points and approvals in advance. Specialists still apply judgement within each step, while the process stays controlled.
A claims operation coordinates damage assessment, policy review and customer communication, with approval where required.
- Define the steps
- Apply checks
- Approve the outcome
Chat becomes workflow
Make proven work repeatable.
Explore the work in Studio first. When the approach proves useful, capture it as a governed workflow, reusing the specialist intelligence and connections already in place.
01
Explore in Studio
Work through a question in chat with specialist agents.
02
Capture the process
Define the proven tasks, handoffs and decision points.
03
Set the controls
Add approvals, exceptions and evidence requirements.
04
Run it again
Trigger the workflow on a schedule, an event or by API.
The process can run without someone prompting each step. People retain the decisions that matter.
- Memory
- Academy
Remember and improve.
Memory and Academy make continuity and controlled improvement part of the operating system. Useful context survives the exchange. Better-performing approaches are tested, versioned and promoted without silently changing live work.
Memory
Persistent context
Context survives the moment.
- Retain relevant history
- Carry forward the interactions, decisions and outcomes that remain useful to future work.
- Keep work products usable
- Preserve datasets, files and generated artefacts so later work can build on them instead of recreating them.
- Support longer objectives
- Let specialists return to an objective with the context needed to continue coherently.
Memory becomes part of the working environment, not a transcript that must be reread.
Academy
Controlled improvement
Improvement stays controlled.
- Test alternatives
- Compare instruction, agent and routing variants against representative work.
- Measure the outcome
- Evaluate quality, reliability, efficiency and the criteria that matter to the capability.
- Promote deliberately
- Version the result and let people decide when a better-performing alternative becomes active.
The system improves without silently rewriting how live work operates.
- Authority
- Governor
Keep control active.
Authority and Governor solve different control problems. Authority keeps access and decisions with people. Governor supervises agent behaviour while work runs. Together they let agents carry more responsibility without weakening accountability.
Authority
Human decision rights
People keep decision rights.
- Define responsibility
- Control who may access, direct, approve, change and inspect each part of the work.
- Place human review
- Require a decision where business judgement or accountability matters.
- Understand dependencies
- See what relies on a resource before changing or replacing it.
Agents carry the work without inheriting authority that belongs to people.
Governor
Runtime supervision
Keep work on course.
- Watch the objective
- Review longer work against the original goal and the grounded facts already established.
- Intervene proportionately
- Stay quiet while work is sound, then request clarification, correction or completion when needed.
- Keep people involved
- Let authorised users add, edit, withdraw or apply guidance before it becomes part of the run.
Supervision supports the work without replacing the person accountable for it.
Governed connections
Define how data works.
A connection carries instructions as well as access. Agents need to understand what a source means, how to use it and where their permission ends.
Bridge connects the systems. Authority defines access and decision rights. Governor supervises the work that uses them.
Portfolio review connection
Illustrative access and usage rules
- Sources
- Portfolio positions, mandate documents and approved market data.
- Meaning
- What each field represents and how positions relate to mandate limits.
- Instructions
- When to use the connection, what to request and which joins are valid.
- Permissions
- Approved roles, permitted data and limits on changes or external access.
- Approvals
- Actions that require an authorised person before they can proceed.
Control during execution
Keep authority in force.
Agents can investigate and prepare a recommendation without gaining permission to carry out every action they propose. Access, supervision and approval work together throughout the run.
01
Set the authority.
Define who can use the capability, what it may access and which decisions need approval.
02
Supervise the work.
Governor reviews progress against the objective and grounded facts, requesting correction or clarification when needed.
03
Retain the decision.
The authorised person reviews material decisions. Guidance, interventions and approvals remain with the work.
Assess a mandate breach.
Read approved positions and limits. Investigate the exposure and prepare a recommendation.
Changing the portfolio requires approval.
The proposed action goes to an authorised person. Permission to investigate does not grant permission to execute a trade.
The work moves forward. Accountability stays clear.
Lens keeps the evidence, guidance and human decisions together so the outcome can be inspected afterwards.
- Lens
Make outcomes defensible.
Lens builds the audit record as work happens. It keeps configuration, evidence, computation and material decisions together, so people can inspect and defend the outcome without reconstructing it afterwards.
Lens
Evidence and evaluation
Keep the record with the work.
- Preserve what ran
- Record the exact resource versions, configuration and workflow path behind the result.
- Keep supporting evidence
- Bring together citations, web sources, Thought Maps, executed code, datasets and step outputs.
- Show material decisions
- Retain Governor guidance, human interventions, completion times and usage beside the work they affected.
Teams can inspect, replay and defend how an outcome was reached.
From request to record
See what actually happened.
A defensible answer needs a visible path behind it: what the agent was asked, which data it used, what it executed and which controls shaped the result.
Lens retains that path as the run progresses, from the first request through the evidence and decisions kept with the outcome.
- Request received
- Connector policy checked
- Context retrieved and joined
- Tools selected
- Generated code recorded
- Code and tool outputs captured
- Response and evidence stored
- Audit record saved
Traceability
Keep the full context.
Inspect the code that ran, the tools called and the data consumed. See how records were joined and which evidence supported the response, alongside the guidance and approvals that affected it.
Runtime telemetry ledger
Illustrative record · retained inside your boundary
- Generated code
- Generated and executed code, inputs, runtime version and execution result.
- Tool calls
- Requests, responses, latency, errors and the policy checks applied.
- Data use
- Connectors, documents, fields, rows and joins used during the run.
- Agent responses
- Intermediate outputs, the final answer and supporting evidence.
- Review and decisions
- Evaluations, Governor guidance, human interventions and approvals.
Evidence in use
Inspect. Evaluate. Defend.
Use the record to understand performance, assess quality and account for material decisions.
Observe
Inspect the run.
Follow agent activity, tool calls, data use and computation. See the outputs, timings and errors behind the result.
Traces · Logs · Metrics · Code outputs
Evaluate
Test the standard.
Assess agent behaviour and output quality against defined criteria. Compare results and identify regressions before and after deployment.
Test suites · Quality scoring · Human review
Audit
Defend the outcome.
Review the sources, resource versions, workflow path and approvals together. Trace the result back to the work that produced it.
Provenance · Replay · Data lineage · Approvals
Your deployment
Inside your boundary.
AlphaAgent OS runs inside your AWS account. The operating foundation, specialist intelligence and customer work remain within your cloud environment.
Customer-controlled boundary
Your AWS account
The operating boundary remains your boundary.
AlphaAgent OS
Intelligence, orchestration, memory, authority, governance and evidence working as one system.
Runs with
- Studio
- Amazon Bedrock
- Your systems
Your AWS account
Agents, conversations, Intelligence Graphs, workspaces, datasets and runtime services remain in customer-controlled AWS accounts, not a shared vendor backend.
Your residency zone
Inference runs through Amazon Bedrock in the selected US or EU zone. There is no direct third-party model API or global fallback.
Protected credentials
Secrets remain in AWS Secrets Manager and are resolved only when authorised work invokes the relevant connection, so access is not broadly exposed to the agent system.
No customer content
Only signed licence heartbeats and usage counts reach Console. Prompts, documents, results, definitions and credentials do not.
Outbound onlySigned licence heartbeats and usage counts only
Start with one capability
Map the whole system.
Bring us a piece of work that matters. We will map the specialist intelligence, execution, connections, continuity, authority and evidence required to run it properly.