Two platforms. One is how agents get built. The other is how work gets done.
Cyberwisdom is an authorised enterprise delivery partner for two agent platforms. We own the deployment, the architecture and the outcome — and we deliver them through the consulting and learning practices that most vendors cannot bring.
Agent Platform
The full lifecycle: build, govern, orchestrate, evaluate, observe
Work Platform
Delivery at the desk: the employee stops searching and starts shipping
Authorised partner
Certified delivery, deployment and support across the region
Consulting + Learning
The operating model and the people, which is where it actually succeeds
Why a consultancy, and not just a reseller
A platform license is a purchase. An operating model is a programme. Most enterprises have bought the first and stalled on the second — and the vendor's own roadmap cannot fix it for them.
We came to this from enterprise learning rather than from software. Twenty-five years of arguing with executives about whether training actually changed behaviour taught us something that turns out to be exactly right for agents: the artefact is not the outcome, and adoption is not capability.
An agent can be technically excellent and change nothing, because nobody redesigned the work around it. That is a management problem before it is a technology problem — and it is the reason our consulting practice exists alongside our delivery practice.
- We deploy it, then we stay to run it. A go-live is not a deployment. The first ninety days of production behaviour are where platforms succeed or die.
- We bring our own architecture judgement. Which agent architecture, which retrieval strategy, which deployment topology — these are engineering decisions with business consequences, and they should not be default settings.
- We connect the platform to your operating model. Work decomposition, authority allocation, escalation paths and evidence are consulting work, not configuration.
- We build the people who own it afterwards. Your team should be able to design, evaluate, operate and govern the estate without us. That is the whole point.
The failure pattern we are built against
We have watched this sequence run in large enterprises more times than we would like to count.
| Stage | What actually happens |
|---|---|
| Pilot | A team proves it can work. Real enthusiasm, small scope. |
| Scale | Every function builds their own agent. No shared semantics, no shared governance. |
| Drift | Agents contradict each other. Nobody can say which is authoritative. |
| Shrink | Budget is cut. The agents with no owner are switched off first. |
| Conclusion | "AI did not deliver." Usually the operating model did not either. |
Every stage on that path is preventable, and every one of them is a management decision rather than a technical limit. That is the argument for buying delivery capability instead of a licence.
How we scope a platform engagement
Described by outcome, not by SKU
Not a platform replacement
Against a pre-go-live baseline
We work with what you have
The platform, in four layers
We describe capabilities, not product names. What follows is what your team needs to be able to do, and in what order.
One produces agents. The other puts them in front of everyone.
Most organisations only buy the first and wonder why adoption stalls. The second is where an agent becomes part of how the business runs.
Enterprise Agent Platform
The runtime and lifecycle layer. Everything an agent needs to be built, approved, measured, observed and controlled — and everything you need to keep a hundred of them consistent.
Six agent architectures
From simple conversational to long-horizon research, plus a supervised multi-agent architecture that dispatches specialists.
Visual orchestration
Model, retrieval, tools, logic and code on one canvas, with human approval breakpoints where the decision warrants it.
Evaluation as a first-class system
Test sets, four kinds of assessor, regression runs and visual reports. Agents get a performance review like people do.
Governance that holds
Agents get departments, roles, responsibilities and access rights — managed in the same model as your people.
Enterprise Work Platform
The desktop where the work actually lands. Your people inherit their own permissions and context, and delegate real deliverables — not just ask questions.
Context from the business, already there
Messages, documents, meetings, schedules, mail, structure. No repeated briefing, no uploading the same policy twice.
Deliverables, not answers
Presentations, spreadsheets, reports, research, dashboards and live applications — produced to a standard you can send on.
It can operate your software
GUI-level interaction with legacy systems that expose no API — running isolated, pausable, and handable to a person at any point.
Enterprise controls
Tenant isolation, inherited permissions, action confirmation levels, quotas, watermarking and behavioural audit.
How the two connect. Methods proven in the Work Platform — the way your best analyst reconciles a ledger, the way your best coordinator runs a close — become reusable skills. Those skills become agents. Those agents get governed on the Agent Platform. This loop is the mechanism by which an organisation's tacit expertise becomes automated at scale, and it is the operational core of what we mean by Human Machine Capital.
Where each capability actually lives
Read across to see which capabilities reinforce each other, and down to see what a single capability area must carry on its own.
| Work surface | Agent runtime | Orchestration | Knowledge | Integration | Evaluation | Cost control | Governance | |
|---|---|---|---|---|---|---|---|---|
| Agent platform | ||||||||
| Work platform | ||||||||
| Build & orchestrate | ||||||||
| Knowledge & context | ||||||||
| Tools & integrations | ||||||||
| Models & compute | ||||||||
| Evaluation & quality | ||||||||
| Observability & cost | ||||||||
| Security & governance | ||||||||
| Deployment & enterprise |
Go deeper on either platform
Eight capability pages covering the parts of the stack that decide whether an agent survives contact with production.
Build & Orchestrate
Agent architectures, the orchestration canvas, multi-agent supervision, and registering agents built elsewhere.
Six architectures, one canvas → 02Knowledge & Context
Enterprise knowledge bases, retrieval tuning at the parameter level, graph-enhanced multi-hop reasoning, and answer traceability.
Where most RAG quietly fails → 03Tools & Integrations
The plugin tiers, what an MCP client and an MCP server each mean in practice, agent-to-agent protocols, and connecting systems of record.
Connecting what you already own → 04Models & Compute
Bringing your own model, private inference inside your network, hybrid deployment, and routing work to the right model tier.
Model choice as an engineering decision → 05Evaluation & Quality
The four-step evaluation method, four kinds of assessor, regression testing, and scanning every conversation for failure.
Agents need a performance review → 06Observability & Cost
Full-chain tracing, root-causing a bad answer, attributing token spend, and the data loop that makes agents improve.
From black box to legible → 07Security & Governance
Identity, role and permission design, encryption and sandboxes, AI firewalls, prompt injection defence and audit evidence.
Controls that were designed, not added → 08Deployment & Enterprise
Hosted, dedicated and on-premise topologies, environment isolation, canary release, and what enterprise-grade actually requires.
Where the contract terms matter →The decision that matters is not which platform
Platforms will converge. Operating models will not. We help you build the part that does not commoditise.
- Licence is the easy part; the operating model is still yours to design
- Context and ontology work is rarely in the licence
- Evaluation harnesses are almost never included
- Supervision and decision logging are configuration, not product
- You need people trained regardless of whose agent it is
- Renewal conversation arrives before the value is proven
- Platform choice becomes a swappable decision rather than a strategic one
- Ontology, context and data engineering are ours to build
- Evaluation is built from your historical cases, on day one
- Supervision model designed jointly with your risk function
- Learning system included, because it is not a platform feature
- The relationship continues on outcomes, not on seat count
The order that actually works
Skip evaluation and you find out about a regression from a customer. Skip context and your agent is confidently wrong.
Agent platform
Where agents run, with identity, tools and memory. The foundation, but not the project.
Knowledge & context
What the agent knows and how it reasons over it — ontology, context engineering, retrieval that survives being wrong.
Build & orchestrate
How a specific workflow becomes a specific agent, with a specific failure mode.
Evaluation & quality
Whether you would know if it broke. Built from your cases, re-run on every change.
Observability & governance
Cost, latency, decision logs and supervision. What makes it operable at 2am.
We hold the certifications. You get the accountability.
We are an authorised enterprise delivery partner for both platforms. That means our delivery teams are certified, our architecture practice is recognised, and our support commitments are contractual rather than best-effort.
It also means something more useful: when a capability gap appears in the platform itself, we have a route to the engineering roadmap that a pure reseller does not. We will tell you when a limitation is ours to fix and when it is upstream.
What we do not do. We do not resell licences and leave. We do not claim to be the platform manufacturer. And we do not take on an engagement where we will be the third party guessing at your business context — if we cannot get access to the people doing the work, we will say so before you sign.
Deployment topologies
| Topology | What it suits | Data position |
|---|---|---|
| Hosted | Fastest route to production; standard data residency options | Provider infrastructure, region of your choice |
| Dedicated | Isolation requirements without full infrastructure ownership | Your virtual network boundary |
| On-premise | Defence, banking, public sector; data sovereignty is non-negotiable | Entirely inside your environment |
| Hybrid | Keep regulated knowledge and inference local, use hosted for the rest | Split deliberately, by data classification |
Topology is an architecture decision with an operational cost. We will recommend the least complex one your constraints actually require — and tell you when a lighter option is enough.
Honest answers to the questions vendors avoid
"Will this replace our systems of record?"
No, and it should not. Agents consume and act through your systems of record; they do not become them. If anyone proposes replacing the system of record with an agent store, that is a red flag about their understanding of data ownership.
"How many agents is too many?"
The number is not the problem — the absence of shared semantics and ownership is. Large estates are manageable when agents inherit one business model; a handful built on conflicting assumptions is already an unmanageable estate.
"What happens when the model changes underneath us?"
Behaviour changes silently. This is why model versions are pinned in production, upgrades are rehearsed in a test environment, and every change is run against your regression set before it reaches users.
"Can we keep this inside our network?"
Yes, including inference. Private deployment runs the platform and the models inside your environment; for mixed requirements we split by data classification rather than arguing about all-or-nothing.
Start from where you are
Six honest starting points. Tell us which describes you and we will begin there rather than at the beginning of our deck.
If you run a contact centre
The highest-volume, most-documented workflow in most enterprises. Ideal first delegation — and the evaluation harness writes itself.
If you have a compliance obligation
Your agent's answers must be defensible. That means governed ontology and a decision log, before any capability conversation.
If your data is messy
The agent will not fix your data. Context engineering will tell you which parts matter and which can be left alone.
If you have already tried and stalled
Almost always a missing evaluation harness. We will tell you which check you skipped, and it is usually the cheapest one to add.
If cost is the objection
Legitimate. Per-task cost is measurable and reducible, and we would rather model it honestly than pretend it is free.
If you need it live this quarter
Possible, with one workflow and an honest scope. Not possible with a platform replacement and a business process redesign.
Why there are no product logos on this page
We do not publish partner product names on this site, and we do not want you to buy a platform because of a logo on a slide. What we will tell you is which capability you need, what it costs to run per task, and what it will not do for you.
What this page answers
Every topic below is covered in the sections above, and each links to the page that goes deeper on it.
Bring one workflow. We will show you both platforms on it.
A ninety-minute working session using your own process and your own documents. You will see the agent build side and the employee delivery side, and you will leave with an honest view of what is realistic.