AI Agents
Digital coworkers that finish the job.
Not a chatbot bolted onto your helpdesk. Agents that read your systems of record, decide what to do, execute it through real APIs, and leave a record any auditor can follow.
- Work resolved without a human touch
- 94%Work resolved without a human touch
- Reduction in cycle time
- 71%Reduction in cycle time
- Enterprise APIs already integrated
- 312Enterprise APIs already integrated
- Actions written to an audit trail
- 100%Actions written to an audit trail
Orchestrator
Routes work, holds budget, owns the outcome
The problem
Most "AI agents" are demos with a login screen.
They work beautifully on the happy path and quietly fall apart on the third exception. The failure is never the model. It is everything around the model: no permission boundaries, no evaluation set, no cost ceiling, no way to explain a decision after the fact. So the pilot impresses the board and never gets production authority.
What it costs you
- Agents given broad credentials because scoping them was too much work
- No regression suite, so every prompt change is a coin flip
- Token spend that nobody notices until the invoice arrives
- Decisions that cannot be reconstructed six weeks later during an audit
- A workflow the team quietly routes around because it cannot be trusted
What we build
The parts that make it survive production.
Every engagement includes all of this. None of it is an upgrade tier.
Task decomposition
We break a real job into steps an agent can plan, execute and verify — with explicit success criteria for each one.
Tool and system integration
Typed, permissioned interfaces onto your ERP, CRM, ITSM, data warehouse and internal services. Agents call functions, not screens.
Policy and guardrails
Deny-by-default permissions, spend ceilings, value thresholds and escalation rules enforced in code before any action executes.
Evaluation harness
Golden datasets, adversarial cases and regression scoring in CI. A prompt or model change ships only when it beats what is already live.
Memory architecture
Working memory, retrieval over your own corpus, and durable state — with an explicit answer for what the agent is allowed to remember.
Human in the loop
Approval routes for anything above the confidence or value threshold, with the full reasoning trail attached to the request.
Cost and latency control
Model routing, caching and step budgets so an agent that thinks too hard about a trivial task gets stopped.
Observability and audit
Every step, tool call, token and decision recorded and queryable — the difference between an experiment and a system you can defend.
Where it pays
Real workloads. Real numbers.
Results are drawn from production engagements and measured against a pre-engagement baseline.
Service desk resolution
Agents read the ticket, check entitlement, run the diagnostic, apply the fix and close the loop — escalating only what genuinely needs a person.
68% of tier-one tickets resolved end to end
Invoice and order processing
Extract, match against purchase orders, resolve pricing exceptions inside tolerance, post the approved batch, and brief the controller.
Cost per document from $2.40 to $0.07
Claims triage
Assemble the file, check policy coverage, flag the indicators that matter, and route to the adjuster with a recommendation and its reasoning.
First-response time cut from 3 days to 4 hours
Revenue operations hygiene
Research accounts, deduplicate records, enrich firmographics and keep the CRM honest without a team of contractors doing it by hand.
4.2 hours per rep per week returned to selling
Security alert triage
Correlate signals, pull host and identity context, dismiss the known-benign, and hand analysts a written case instead of a raw alert.
92% of alert noise cleared before human review
Procurement intake
Interpret the request, apply policy, gather approvals, check the preferred-supplier list and raise the requisition in your ERP.
Requisition cycle from 9 days to 2
How it runs
From first conversation to running system.
- 01Week 1
Map the work
We sit with the people doing the job and record what actually happens — including the exceptions nobody documented. You get a systems map and a measured baseline.
5 days
- 02Week 2
Set the boundaries
Permissions, spend ceilings, escalation thresholds and the explicit list of things the agent must never do. Signed off before a line of agent code exists.
4 days
- 03Weeks 3–6
Build and evaluate
Tools, planning loop and policy layer, developed against an evaluation set drawn from your real history. Scored every build, not vibes-checked at the end.
3–4 weeks
- 04Week 7+
Run it in the light
Shadow mode first, then supervised, then autonomous within its boundary. Dashboards, alerts and a runbook your team owns from day one.
Ongoing
Technology
Chosen by evaluation, not by preference.
We build on what fits your constraints and what your team can maintain. Nothing here locks you in.
- Your repositories, your cloud account, your licence
- No proprietary runtime you have to keep paying for
- Documentation written for the engineer who inherits it
Models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Open-weight, self-hosted
Orchestration
- LangGraph
- Temporal
- Model Context Protocol
- Custom runtimes
- Event-driven queues
Memory & retrieval
- Postgres + pgvector
- Pinecone
- Elasticsearch
- Redis
- Hybrid search
Runtime & delivery
- TypeScript
- Python
- Kubernetes
- AWS Bedrock
- Azure AI Foundry
- OpenTelemetry
How we price it
Three ways in. A stop point at each one.
We price agent work in three stages so you can stop at any point without stranded cost. Roughly 30% of pilots end at stage one with a recommendation not to proceed — that is the system working.
Proof
Fixed price · 3–4 weeks
One workflow, measured honestly. You get a working agent in shadow mode, a baseline comparison and a straight answer on whether the economics hold.
- Process mapping and baseline measurement
- One agent, one workflow, shadow mode
- Evaluation set built from your history
- Written go / no-go recommendation
Build
Fixed scope · 8–16 weeks
Production deployment with the guardrails, integrations and observability required to give an agent real authority in your business.
- Full tool and system integration
- Policy, permissions and spend controls
- CI evaluation and regression suite
- Dashboards, alerting and runbooks
- Team training and handover
Operate
Monthly · rolling
We keep it healthy: model updates, evaluation drift, new edge cases and cost tuning. Cancel with 30 days notice, and you keep everything.
- 24/7 monitoring and on-call
- Monthly evaluation and drift review
- Model and prompt version management
- Quarterly cost optimisation
- New workflow additions at agreed rates
Questions
What buyers ask about ai agents
Direct answers, including the ones that are inconvenient for us.
Still deciding?
Send the question to a senior engineer instead of a form. You will get a straight answer, and a no if that is the honest one.
Built-in assistants help a person do a task faster. An agent completes the task. The distinction matters commercially: one improves handle time by 10–20%, the other removes the handle entirely for the 60–90% of cases that follow a known pattern. We often recommend keeping the vendor assistant and adding agents only where the volume justifies it.
Three layers. Permissions are deny-by-default and scoped per tool, so an agent literally cannot call an API it was not granted. Value and confidence thresholds route anything material to a named human approver. And every action runs through a policy check that can block, log or require dual approval. We also build the reverse path — if an action can be taken automatically, it can be undone automatically.
Whichever one wins the evaluation for your workload, and it is usually not the same model for every step. We route cheap classification to small fast models and reserve frontier models for genuine reasoning. Your system is built so that swapping a model is a configuration change, not a rewrite — that is deliberate, because the leaderboard changes every few months.
Yes. We deploy into your cloud account or on-premises, with open-weight models served locally where data residency or regulation requires it. You own the infrastructure, the code and the weights. Expect a performance trade-off on the hardest reasoning steps, and we will quantify it before you decide.
Shadow mode within four weeks, supervised production between weeks eight and twelve for a typical workflow. We deliberately do not push for autonomy earlier — the shadow period is where you find the exceptions that would otherwise become incidents.
That is your decision and we will not pretend otherwise. In practice most clients redeploy rather than reduce, because the work that remains is the judgement-heavy part that was always being rushed. We will give you honest volumes so you can plan properly rather than discovering the impact after go-live.
By making it structurally difficult to matter. Agents act through typed tools with validated inputs, so an invented value fails schema validation rather than reaching your ERP. Anything factual is retrieved and cited rather than recalled. And the verification step scores output against known-good outcomes before it commits. Residual risk is bounded by the value thresholds, not eliminated by optimism.
No. We build in your repositories, on your infrastructure, using open standards. There is no CyberXSolutions runtime you have to keep paying for. If you end the relationship, everything keeps running and your team has the documentation to change it.
Related
- AI AutomationDeterministic workflows for the work that does not need judgement
- Enterprise SolutionsRolling agents out across a whole organisation
- AI CybersecurityAutonomous detection and response
- Our processHow an engagement actually runs
- Case studiesMeasured results from production systems
- TechnologiesThe stack behind the systems
Start the conversation
Bring us the ai agents problem you have already tried to solve.
Ninety minutes with our engineers. You leave with a systems map, a shortlist and an honest read on whether this is worth doing at all.
What to expect
- No pitch deck, no obligation
- Senior engineers in the room
- A written plan within five days
Prefer email?
support@cyberxsolutions.us