Everyone has an AI pilot. Almost no one has an agent in production.
The Kavuka Agent is born on the other side of the gap: built on the platform’s engines and data, with a defined task scope, explicit autonomy limits and an auditable trail of every action — the autonomy your reviewer approves.
- End to end
- complete tasks executed
- Autonomy limits
- made explicit
- Every action
- logged and explainable
- In months
- payback measured, not promised
Agents in production on the platform’s engines and lakehouse — investigation, onboarding and compliance executed with scope, autonomy limits and a full trail of every action.
They adopted AI. But the pilot is still a pilot.
The eternal pilot
The demo wowed the board, but six months later it is still stuck: without trusted data and a defined scope, the prototype never becomes a product.
Senior staff stuck in the repetitive queue
The backlog grows faster than headcount, copy-paste between systems eats the team and the competitor already operates at a lower marginal cost.
The black-box agent legal blocked
Broad access and autonomous action with no trail, an unexplainable automated decision and no clear owner for the agent’s mistakes stall the project at review.
Cost Gartner forecasts that roughly 40% of agentic AI projects will be cancelled by 2027 — and the vast majority of companies that adopted agents do not have them in production. Adopting without a foundation means paying for the pilot, the frustration and the internal skepticism that delays the next attempt. How many pilots has your company already buried?
From the right process to governed autonomy, in one design.
- 01
Choose the process
The task with volume, rules and pain — the right candidate, not the ambition to automate everything at once.
- 02
Design the agent
Task scope, tools, autonomy limits and the human positioned at the right point in the flow.
- 03
Anchor
The platform’s engines and data as tools, the lakehouse as memory — the ground the cancelled projects never had.
- 04
Operate with governance
A per-action trail, explainable decisions, monitored cost and continuous improvement — the autonomy the reviewer approves.
What the agent executes
The LLM as the brain, the engines and APIs as hands: the agent perceives context, plans, uses the right tools and delivers the complete task — with the human on the exception.
Investigation Agent
Queries the engines, pivots and builds the dossier with sources
Onboarding/KYC Agent
The end-to-end pipeline with exception handling
Compliance Agent
Regulatory change triaged, contextualized and routed
Operations Agent
Multi-system backoffice routine with RPA and APIs
Data and lakehouse
The ground of autonomy: trusted data as memory
Autonomy limits
The agent decides as far as it was authorized
Trail and explainability
Every action logged: what, when and on what basis
Custom agents
The agent designed around your process
Where the agent reaches production
Investigation & risk analysis
The dossier assembled by the agent, decided by the human — the use case where the platform already has the engines and the data.
Onboarding with exceptions
The full flow with the agent handling the out-of-pattern case: the hard case analyzed and routed with an opinion.
Multi-system operations
The cross-system routine executed with RPA and APIs as the agent’s tools — legacy integrated without a rewrite.
Internal support
The agent that resolves with context and escalates with judgment — autonomy calibrated by each task’s limits.
The governance that separates production from demo
In a market of demos, governance is the reviewer’s differentiator. The Kavuka Agent was designed for the audit that autonomy requires — and that most projects discover too late. Compliance is not a report at the end: it is how the agent operates.
- Configurable autonomy limits and human supervision at the right point in the flow.
- A per-action trail: every step of the agent logged — what, when and on what basis.
- Explainable decisions and the cost of each run monitored.
- Data-protection law in data access: legally permitted sources, encryption in transit and at rest.
- Defined accountability: each flow with an explicit owner of the error and the exception.
The pilot that ran for a year going nowhere reached production in weeks once we anchored the agent in the data and the autonomy limits.
The agent builds the dossier and runs the queue; my senior analysts were left only with the exception and the decision that requires judgment.
For the first time I approved an autonomous agent: every action has a trail, every decision is explainable and accountability has an owner.
Bring a real process. We return the agent blueprint — with the estimated payback.
In 15 minutes you see the path from pilot to production in your scenario: scope, autonomy limits and trail.
- For businesses only. No purchase commitment.
- Data used solely for commercial contact.
- Enterprise leads answered within 1 business day.
What an AI Agent is and why so few reach production
An AI Agent is an artificial-intelligence system with goal-oriented autonomous behavior: it perceives context, plans, uses tools (queries, systems, APIs), executes multi-step tasks and delivers the result — with human supervision calibrated to the task’s risk. This is what separates the agent from the chatbot, which only answers, and from traditional automation, which follows a fixed script: autonomy, decided within the limits the agent was given.
The category’s moment is defined by a number — and by a gap. Gartner projects that 40% of enterprise applications will have embedded agents by the end of 2026, up from under 5% a year earlier, and the intent curve is the most aggressive among emerging technologies: about 17% of organizations already have agents deployed, but more than 60% plan to adopt within two years. Agentic AI sits at the peak of inflated expectations. The real problem, however, is not adoption: it is the production gap. The vast majority of companies that “adopted” agents do not have them running in production, and Gartner itself forecasts that roughly 40% of agentic AI projects will be cancelled by 2027.
The reason for failure is structural, not technological — and it comes down to three missing pillars. First, data: the agent is only as good as what it queries; dirty or nonexistent bases doom the project before the model. Second, scope and limits: the ambition to “automate everything” without a defined task and without explicit autonomy limits produces impressive demos that never operate. Third, governance: the lack of a trail, of explainability and of defined accountability is exactly what the security, legal and compliance team — the reviewer — blocks at review. The defining signal of the cycle is precisely the rise of these governance, security and cost profiles alongside the technology: production maturity changed the game.
The Kavuka Agent is born on the other side of that gap. It is not the generic framework, but the vertical agent with ground beneath it: the investigation, onboarding and compliance agent built on the platform’s engines and data — and the sectors leading in real production, banking and insurance, are exactly the platform’s home. The three pillars are the product as much as the agent: trusted data as foundation, scope and limits in the design of each flow, and governance with a per-action trail, explainable decisions and monitored cost. The result is the complete task delivered, the senior team freed from the queue for the work that requires judgment, the reviewer with the audit they asked for — and payback measured in months, the category standard in operations functions. The agent that reaches production because it was designed for it.
What sets an agent apart from a chatbot or an automation?
The agent pursues a goal with autonomy: it perceives context, plans, uses tools and executes multiple steps — it does not just answer (chatbot) nor follow a fixed script (automation). Autonomy is calibrated by limits: the agent decides as far as it was authorized.
Why do so many agent projects fail?
Because of three missing pillars: data (the agent querying dirty or nonexistent bases), scope (the ambition to “automate everything” with no defined task) and governance (the lack of trail and accountability that the reviewer blocks). The market forecast of around 40% cancellations by 2027 portrays this gap.
Does the agent replace people?
The agent runs the repetitive queue; the person keeps the exception, the review and the decision. The explicit design of each flow defines where the human steps in, and the category’s experience shows senior staff reallocated to work that requires judgment.
How does governance work?
Every agent action is logged (what, when, on what basis), decisions are explainable, cost is monitored and limits are configurable — the full trail for audit, compliance and the accountability that autonomy requires.
Do I need the whole Kavuka platform to have an agent?
No. The agent is born on your process and your systems, using the Kavuka engines as tools where they add value (verification, data, decision). The full foundation amplifies it, but the first agent starts with one process and one payback.
How long until the agent generates a return?
The category standard in operations functions is payback measured in months, not promises. We start with the candidate process — the task with volume, rules and pain — so the first agent delivers measurable results fast.
How does the agent connect to my legacy systems?
The platform’s engines and data come in as tools, and RPA and APIs work as the agent’s “hands” on legacy — the multi-system routine executed without rewriting existing systems, with the LLM as the orchestration brain.
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