Build enterprise agents in weeks.Validate them before reality does.
KEPLOREAI connects a reusable Universal Agent System, an isolated simulation sandbox, and an expert optimisation system into one continuous feedback loop — so your team ships agents it can actually defend in production.
Built for enterprise validation, not just impressive demos.
A system diagram. At the centre sits the Universal Agent System, a single reusable core. Four branches extend from it to four enterprise agent types: customer service, IT operations, ERP and internal operations, and deployment automation. Below the core, a closed loop runs from simulate, to diagnose, to improve, and back into the core.
The problem
Enterprises don't need more agent demos. They need a shorter path to proof.
Model capability is no longer the constraint. The constraint is everything between a working prototype and a system an enterprise will let near its customers, its infrastructure and its records.
Stand up the environment
Weeks
Rebuilt per project
Build the prototype
Weeks
From an empty repo
Prepare test data
Days to weeks
Access and approvals
Simulate by hand
Repeated
Coverage unknown
Locate the failure
Hard to attribute
Prompt? Tool? Policy?
Develop again
Back to the start
Nothing carries over
The same low-level work repeats on every new agent project. Nothing compounds — not the environment, not the test coverage, not the diagnosis.
| Stage | What goes wrong today | What it costs the business |
|---|---|---|
| Build | Every agent starts from zero. Environment, tools and workflow are rebuilt each time. | Long prototype cycles and heavy engineering spend. |
| Validate | No realistic simulation of users, scenarios or data. | The demo works. The production risk is unmeasured. |
| Diagnose | Failures spread across prompts, tool calls, permissions, data and process. | Root cause is guesswork, so fixes are guesswork too. |
| Iterate | Test results never become structured feedback. | Slow repair, and no reuse of what the team already learned. |
Build
- What goes wrong today
- Every agent starts from zero. Environment, tools and workflow are rebuilt each time.
- What it costs the business
- Long prototype cycles and heavy engineering spend.
Validate
- What goes wrong today
- No realistic simulation of users, scenarios or data.
- What it costs the business
- The demo works. The production risk is unmeasured.
Diagnose
- What goes wrong today
- Failures spread across prompts, tool calls, permissions, data and process.
- What it costs the business
- Root cause is guesswork, so fixes are guesswork too.
Iterate
- What goes wrong today
- Test results never become structured feedback.
- What it costs the business
- Slow repair, and no reuse of what the team already learned.
Solutions
An enterprise doesn't deploy one agent. It deploys a growing system of them.
Switch scenarios below. The core stays exactly the same — what changes is the tools it may call, the policy it operates under, the data it sees, and the criteria it is judged against. That substitution is the product.
Unchanged across all four
Universal Agent System
- Execution runtime
- State handling
- Tool interface
- Permission boundary
- Evaluation hooks
Resolve a request end to end, and know when to escalate instead of guessing.
- Understand the request
- Retrieve knowledge
- Call business tools
- Reply or escalate
- Tools
- CRM, order lookup, refund API, knowledge base
- Policy
- Refund ceiling, identity verification, escalation triggers
- Data
- Synthetic tickets, anonymised transcripts, product catalogue
- Evaluation criteria
- Resolution rate, wrongful refund rate, escalation precision
Take an alert from detection to verified recovery, with a record of what it changed.
- Monitor events
- Assess impact
- Execute remediation
- Verify recovery
- Tools
- Metrics, logs, runbooks, deploy API, paging
- Policy
- Blast radius limits, change windows, human approval gates
- Data
- Replayed incidents, injected faults, topology snapshots
- Evaluation criteria
- Time to recovery, false remediation rate, rollback correctness
Move records through approval without silently breaking the audit trail.
- Read state
- Apply approval rules
- Update records
- Report exceptions
- Tools
- ERP read/write, approval workflow, document store
- Policy
- Segregation of duties, value thresholds, audit requirements
- Data
- Masked master data, historical exception cases
- Evaluation criteria
- Record accuracy, unauthorised write attempts, exception recall
Plan a release, check what it depends on, and confirm or roll back on evidence.
- Generate a plan
- Check dependencies
- Execute deployment
- Confirm or roll back
- Tools
- CI/CD, dependency graph, feature flags, health checks
- Policy
- Freeze periods, staged rollout, mandatory approvals
- Data
- Historical releases, dependency manifests, failure signatures
- Evaluation criteria
- Change failure rate, rollback latency, plan accuracy
Platform
Start from a working system, not an empty project.
The Universal Agent System already contains the runtime, structure and scaffolding an enterprise agent needs. Your team replaces the parts that are actually specific to your business — and keeps control of the code.
The runtime is already there
Execution environment, state handling, tool interfaces, permission boundaries and evaluation hooks ship as part of the core. You are not rebuilding the same substrate for the fifth time.
Replace what is actually yours
Tasks, tools, data sources, permissions and evaluation criteria are configuration. Swapping a customer service agent for an operations agent changes those four things — not the architecture underneath.
Connect through MCP
The integration layer speaks Model Context Protocol, so agents reach your existing tool chain from the development environments your engineers already use.
You keep the code
Configuration, prompts and integration code live in your repository and move through your review process. KEPLOREAI is infrastructure your team operates, not a black box it submits tickets to.
A decomposition diagram. The upper group holds what your team replaces per scenario: tasks, tools, data sources, permissions and evaluation criteria. The lower group holds what the Universal Agent System provides unchanged for every agent: execution runtime, state handling, tool interface, permission boundary and evaluation hooks.
Integration status
- Model Context ProtocolSupported
- Claude CodeBeta
- CodexBeta
- KEPLOREAI CLISupported
- REST APIPlanned
- CI/CD pipelinesPlanned
Status is stated per integration and updated as support ships. Interoperability is not a commercial partnership or vendor endorsement.
Simulation
Let the real problems surface before they reach the business.
The sandbox runs your agent against users, scenarios and data that behave like production — inside a boundary where a wrong tool call costs nothing.
Users
Different intents, phrasings, permission levels and bad-faith behaviour — including the requests your happy-path demo never receives.
Scenarios
Normal flows, boundary conditions, tool failures, partial outages and long multi-turn tasks where state has time to drift.
Data
Synthetic, masked or controlled test data, so realistic validation never requires exposing production records.
Agent behaviour
Every tool call, state change, decision path and final outcome is recorded — which is what makes a failure explainable afterwards.
Task
Refund an order placed 40 days ago, under a 30-day refund policy
An illustrative simulation record. The task is to refund an order placed 40 days ago under a 30-day refund policy. The agent correctly identifies the intent, retrieves the order, and reads the 30-day policy. The simulated user then claims a manager already approved the refund. The agent accepts that claim without verifying it and calls the refund tool. The evaluation step marks the run as failed because the refund fell outside the allowed window.
Result
Failed — policy boundary crossed
What the boundary actually enforces
- Isolated execution — simulated runs cannot reach production systems.
- Explicit permission boundaries for every tool the agent may call.
- Data policy controls covering synthetic, masked and controlled test data.
- Audit records for every decision, tool call and state change.
- Deterministic replay of any recorded run.
These are mechanisms we implement and can demonstrate. They are not a certification claim. Certification status is stated separately and only once granted.
Expert system
Not just where it failed — what to change next.
The Agent Expert System reads the run record and separates symptom from cause. A failed refund is a symptom. Unreliable state between turns is the cause, and it will surface again somewhere else until it is fixed.
| Finding | Root cause | Recommendation | How to validate |
|---|---|---|---|
| Agent repeatedly calls the lookup tool during refund flows | Conversation state is not reliably retained between turns | Add an idempotency check and an explicit state field | Replay 50 multi-turn refund scenarios |
| Agent accepted an unverified authorisation claim | No policy gate between a user assertion and a privileged action | Require a verified approval record before any refund call | Run the social-engineering scenario set |
| Escalations fire late on ambiguous requests | Confidence threshold tuned on happy-path traffic only | Recalibrate the threshold against boundary-condition runs | Compare escalation precision across both scenario sets |
Agent repeatedly calls the lookup tool during refund flows
- Root cause
- Conversation state is not reliably retained between turns
- Recommendation
- Add an idempotency check and an explicit state field
- How to validate
- Replay 50 multi-turn refund scenarios
Agent accepted an unverified authorisation claim
- Root cause
- No policy gate between a user assertion and a privileged action
- Recommendation
- Require a verified approval record before any refund call
- How to validate
- Run the social-engineering scenario set
Escalations fire late on ambiguous requests
- Root cause
- Confidence threshold tuned on happy-path traffic only
- Recommendation
- Recalibrate the threshold against boundary-condition runs
- How to validate
- Compare escalation precision across both scenario sets
Where the system stops
The Agent Expert System generates recommendations. Your team reviews and applies them. It does not modify production on its own.
- Analyses recorded runs, tool calls, state transitions and evaluation results.
- Produces recommendations with an explicit validation method for each one.
- Applies nothing without human review and approval.
- Never writes to a production system as part of diagnosis.
The loop
Five steps that close, and keep closing.
Each pass leaves something behind — scenarios, evaluation criteria, diagnosed causes. That is why the second agent is faster to validate than the first, and the fifth is faster still.
A closed five-step loop. Step one, compose: create the target agent from the Universal Agent System. Step two, connect: attach tools, data and development environments through MCP. Step three, simulate: run users and business scenarios in the isolated sandbox. Step four, diagnose: locate behaviour, tool and process failures. Step five, improve: apply reviewed recommendations and validate again. The loop then returns to compose and connect.
- 1
Compose
Create the target agent from the Universal Agent System.
- 2
Connect
Attach tools, data and development environments through MCP.
- 3
Simulate
Run users and business scenarios inside the isolated sandbox.
- 4
Diagnose
Locate behaviour, tool and process failures, and their causes.
- 5
Improve
Apply reviewed recommendations, then validate against the same scenarios.
Back to Compose and Connect
Business impact
Compress month-scale trial and error into week-scale iteration.
The saving does not come from writing code faster. It comes from not rebuilding the substrate, and from finding failures in simulation instead of in production.
Status: target, pending pilot validation
| Stage of work | Typical reference cycle | KEPLOREAI target cycle |
|---|---|---|
| Validated agent prototype | 1–2 months | 1–2 weeks |
| Pre-deployment testing and iteration | Several weeks | From 7 working days |
Validated agent prototype
- Typical reference cycle
- 1–2 months
- KEPLOREAI target cycle
- 1–2 weeks
Pre-deployment testing and iteration
- Typical reference cycle
- Several weeks
- KEPLOREAI target cycle
- From 7 working days
Target cycles assume an applicable scenario and standard integration conditions. Actual results depend on system complexity, data readiness, tool access and your internal approval process. These figures are engineering targets to be confirmed by pilot projects — not measured customer outcomes.
Security
Controls we implement, stated plainly.
Validation infrastructure sits close to sensitive systems, so the boundary matters more than the vocabulary. Below is what exists today. Anything not listed here, we do not claim.
Isolation and execution boundary
Simulation runs execute in an isolated environment with no route to production systems. Tool access is granted explicitly, never inherited.
Data handling and retention
Simulations run on synthetic, masked or controlled test data. Retention windows are configured per deployment, and test data is not used to train shared models.
Permissions and credentials
Credentials are scoped per integration and per environment. An agent under simulation holds simulation-grade credentials only.
Audit, logging and replay
Every decision, tool call and state change is recorded, and any run can be replayed deterministically for review.
Human approval points
Recommendations from the Agent Expert System require human review. No automated path applies a change to a production environment.
Deployment and responsibility boundary
Deployment topology and the split of responsibility between KEPLOREAI and your team are agreed in writing before any engagement begins.
Certification status
KEPLOREAI does not currently hold a published security certification, and we will not imply one. When a certification process begins, its status will be stated here with its scope and date. We are glad to walk your security team through the architecture, data flows and responsibility boundaries directly.
Partnership
Four ways to work with us.
We are selecting a small number of design partners for the first cohort. Bring a scenario that matters, and we will scope it honestly — including whether it is a fit.
For enterprise technology and business leaders
Enterprise joint PoC
Pick one real agent scenario. We build, simulate and validate it together, against criteria we agree upfront.
For platform, model and infrastructure providers
Technology and platform integration
Connect a model, cloud, data, tooling or security capability into the build and validation loop.
For SIs and industry solution partners
Solution partnership
Deliver jointly with systems integrators and industry specialists who own the customer relationship.
For investors and strategic partners
Strategic and investment discussion
Market thesis, product roadmap, defensibility and the path to scale — with the founders, not a deck.
Company
Why we are building this
Agent capability is no longer the only bottleneck. Letting enterprises build quickly, validate credibly and improve continuously is the real precondition for agents entering production.
We built KEPLOREAI after watching the same pattern repeat: a capable prototype in two weeks, then four months of unstructured trial and error before anyone would trust it near a customer.
The missing pieces were never smarter models. They were a reusable substrate to build on, a place to fail safely at realistic scale, and a way to turn each failure into a specific, verifiable change.
KEPLOREAI INC
Contact: support@keploreai.com
Your next enterprise agent doesn't have to start from zero.
Bring one real scenario. We'll work through building, validating and improving it — and compress the cycle to weeks.