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An AI assistant is planned. Until it is genuinely useful we would rather point you at the page that actually answers your question.
LLM application development covers systems where a language model is a component in a larger program rather than a chat window. The model decides which tool to call, interprets the result, and continues until a task is complete or a limit is reached.
That distinction matters because the failure modes change. A model answering a question can be wrong. A model with tool access can be wrong and take an action — issue a refund, send an email, update a record. The engineering effort moves from prompting to constraint design.
The commercial appeal is tasks that require several steps and some judgement: reconciling a discrepancy across systems, triaging and acting on a request, gathering information from multiple sources before drafting a response. These sit between simple automation and genuine human work.
The corresponding risk is that autonomy multiplies the blast radius of a mistake. A wrong answer is a bad experience; a wrong action is an incident. Systems that take actions need permission boundaries, spending limits and audit logs from the first release, not after the first problem.
Our approach
We give the model the narrowest possible tool surface. Each tool does one thing, validates its own inputs, and enforces permissions independently of what the model requested. The model proposes; the tool decides whether it is allowed. Trusting the model to stay inside boundaries is not a security model.
We manage context deliberately. Long conversations and large retrieved documents degrade both accuracy and cost. Summarising history, retrieving selectively and pruning aggressively keeps quality up and spend proportional.
We log every step for replay. When an agent produces a wrong outcome, you need the full trace — what it saw, what it chose, what came back — to diagnose it. Without that, debugging is guesswork against a non-deterministic system.
Capabilities
Typed tool definitions with server-side validation and permission checks independent of the model.
Multi-step tasks with step limits, spend caps and defined stopping conditions.
Summarisation, selective retrieval and pruning so long sessions stay accurate and affordable.
Streaming responses, interruption handling and state that survives a page refresh.
Full step-level traces so any outcome can be reconstructed and diagnosed.
Scoring whole trajectories rather than single responses, since a correct answer via a wrong path is still a defect.
Stack
Process
What the agent may do, what it may never do, and where a human must approve before an action proceeds.
Narrow, validated tools that enforce permissions themselves rather than relying on model compliance.
Test cases scoring the whole sequence of steps, not just the final output.
Step limits, spend caps and timeouts in place from the first working version.
Human approval on every action initially, relaxed only where the evidence supports it.
Success rate, cost per task and intervention rate tracked continuously after launch.
Use cases
Handling routine requests end to end — looking up an account, applying a change, confirming it — within strict limits.
Comparing records across systems, investigating discrepancies and proposing corrections for approval.
Gathering information from several internal sources and producing a cited briefing.
Assistants with read access to company systems that answer questions staff would otherwise ask a colleague.
Choosing an approach
The right level depends on the cost of a mistake. This is a product decision as much as a technical one, and it should be made explicitly.
| Level | Appropriate when | What you accept |
|---|---|---|
| Read-only | Answering questions and producing summaries | Staff still perform every action manually |
| Propose and approve | Actions are consequential but repetitive | A person remains in the loop, so throughput is bounded by review capacity |
| Bounded autonomy | Low-value, high-volume actions with a proven success rate | Some mistakes will happen; limits and audit logs contain them |
Outcomes
FAQ
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