Give AI a job.
Keep the controls.
Turn approved information into job briefings, enquiry drafts and useful next steps. Nordlith designs AI agents around a defined workload, with people in control of the decisions that matter.
An illustrative workflow. Permissions and approval points are agreed for each implementation.
An agent is not just a chat window
An agent may use several steps or tools to complete a bounded task: find a record, compare information, prepare a response, then ask for approval. Tool access makes clear permissions and exception handling more important, not less.
Choose a job with an observable finish
A good first task has identifiable inputs, a named owner and a result you can check. Examples include preparing an enquiry response from approved information, assembling a job briefing or flagging missing details for review. Choose one that your team can assess against the way it works today.
Agent, automation or operator?
Use ordinary automation for stable rules. Consider an agent when interpretation or several context-dependent steps are useful. An operator coordinates recurring work over time. The three can work together, but each needs a distinct responsibility.
The boundaries belong in the build
Define allowed records and tools, maximum task scope, approval points and stop conditions. Test incorrect inputs, ambiguous identities, denied permissions and unavailable services. Keep consequential sends and writebacks human-approved unless a narrower automatic action is deliberately authorised.
What a first implementation should leave you with
An agreed task definition, connected inputs, tested behaviour, a review path and operating notes for the person who owns it. Agree the deployment, support owner and escalation path as part of that first scope.
Put your team’s knowledge to work
Help staff find the current procedure, prepare a job briefing or draft an answer from approved information. A useful first scope is one source collection, two staff roles and a set of questions your team can check. The result should show where an answer came from and when someone needs to resolve a gap.
Choose the operating model before the model name
Managed workspace tools. Start by checking what your existing workspace and licences already provide. Assess its search coverage, identity controls, retention settings and administrator responsibilities against the actual plan. Your team still owns source quality and access decisions.
Permission-aware retrieval. A tailored application can retrieve approved material and show source references alongside an answer. The design must carry the user’s permissions through search, indexes and cached snippets. Someone needs to own source refresh, deletion and changes to access.
Self-hosted models. Consider this route when control requirements justify operating the infrastructure. Hardware capacity, model licences, patching, monitoring and tested backups become part of the job. Compare the complete operating responsibility, not just where a server sits.
The right route depends on your information, people and operating capacity. We assess fit before proposing a deployment; an Australian hosting location or a particular workspace feature must be verified for the chosen design.
An answer you can trace. Access you can test.
Bring a redacted source sample, common staff questions and an example of information that one role may see but another may not. We can then define the retrieval boundary, demonstration and operating handover around the work.
Test two roles against the same questions
Check answers against the authorised source version. Include missing and conflicting documents, then revoke access and check that an old snippet cannot still be retrieved. A useful assistant should ask for clarification or decline when the evidence is insufficient, rather than fill the gap with a plausible answer.
Make the data boundary explicit
Agree which content reaches model providers, retrieval indexes, logs, support staff and backups. Residency, retention, training use and human access are separate decisions. Match each requirement to the actual service settings and operating owner.
Leave the owner with a usable handover
A first implementation should leave you with an agreed question set, tested role behaviour, source references and operating notes. Name who refreshes sources, removes access, handles incidents and dependency updates, tracks recurring costs and tests restoration. Start with a demonstrable knowledge task before adding tools that can change records.
Discuss a knowledge-assistant scope ↗ · Explore connections to business records ↗
Before we start.
Can it work with our existing tools?
Often that is the right goal, but access and compatibility must be assessed for the actual product and plan. Name the systems in your enquiry without sharing passwords.
Will an agent remove the need for staff?
That is not the starting promise. The goal is useful support for a defined workload, with people retaining judgment and accountability.
How do we know when not to use AI?
If the rule is stable and deterministic, conventional automation may be simpler. If the source information is unreliable or consequence cannot be controlled, fix those conditions first.
A useful next step.
What would be easier
if this worked properly?
Tell us where time or follow-through is being lost. We will start with the problem, not a product pitch.
Talk to Nordlith →