Define the agent job
We identify the beat, user, entity scope, watch conditions, expected outputs, and escalation rules. The agent is a named role — not a generic chatbot you prompt.
← All services
Agents
Custom Agents
A useful agent is not a generic chatbot. It is a designed role with a beat, context, tools, limits, and outputs. We work with the client to define that role and make it operational.
Client question
What would you assign to a perfect analyst if you could hire one for this exact beat?
We translate the client need into an agent role: what it watches, what it knows, what it can draft, what it can do, and where a human must approve.
Outcome
A custom agent with a defined job, data access, reasoning context, tool policy, approval model, and first production output.
In practice
What this looks like for a real client.
A mid-sized manufacturer runs a complex order book across multiple production lines. Every morning before 6 AM, the operations director needs to know: which orders are at risk of missing their delivery promise, which work orders are running behind, which materials will run short this week, and which supplier shipments are delayed. Staffing an analyst for this beat is impossible. We designed an AI Operations Analyst agent that watches the order book, work orders, inventory levels, and supplier deliveries continuously. It drafts a morning briefing with the at-risk list, writes back updated promise dates to the ERP, and holds customer update drafts for one-click approval. One agent, one beat, governed action — running every day.
How it works
We design around how the work actually moves.
Build the knowledge and context
We encode the domain rules, company language, data model, operating thresholds, and examples the agent needs to reason usefully. This is not prompt engineering — it is role design.
Design tools and approvals
We decide what the agent can read, draft, publish, write back, or only recommend. Consequential actions (payments, external comms, system changes) require explicit human approval.
Pilot the agent cycle
We run the first briefing, alert, recommendation, or action queue with client review, then tune the behaviour. The agent gets sharper with each cycle as confirmed decisions feed back.
Role of AI
Not a generic chatbot. Specific techniques, applied to specific data.
The agent operates on a defined beat — it does not "answer any question." It watches specific entities (orders, work orders, inventory, shipments) against configured conditions (idle > 24h, operation overrun, material cover < X days). It reasons over a typed workspace schema, not raw text. Tool grants are deny-by-default: the agent can read entities, draft publications, and write back status updates, but any external communication or financial action is held for human approval. The agent's behaviour improves over time as confirmed matches and corrections feed back into its reasoning context.
Selected engagements
Not slides. Shipped work, running in production.
Pharma
Secondary Sales Enrichment Agent
4-stage matching pipeline: exact code → lookup cache → fuzzy search → LLM assist
Product match rate improved from 70% to 94%+. Self-learning: human confirmations cached for future runs.
Pharma / CPG
Scheme Claim Validation Agent
150,000-200,000 invoices processed monthly with AI audit pipeline
Fraudulent and duplicate claims flagged before settlement. 40-person manual team replaced.
Manufacturing
Manufacturing Operations Analyst Agent
Watches orders, work orders, inventory, and shipments continuously
Daily morning briefing auto-drafted. At-risk orders flagged with updated promise dates. No analyst hire needed.
Deliverables
Shipped at the end of the engagement
- Agent role brief with beat, goals, data scope, and outputs
- Knowledge pack and examples specific to the client operation
- Tool and approval policy defining exactly what the agent may do
- Pilot agent run with review notes and tuning backlog
What we need
What we need from your team to begin
- Named business owner for the agent beat
- Examples of good and bad decisions in the workflow
- Data access and records the agent should watch
- Approval rules for external, financial, or system-changing actions
Timeline
A practical delivery cadence, not an open-ended discovery.
Role and beat design
Agent workshop, sample cases, output definition, and approval model. A working agent spec by Friday.
Agent build and context tuning
Knowledge, workflow, data access, tool policy, and test runs on representative scenarios.
Pilot cycle
First production briefing, alert, or action queue with client review and behaviour tuning.
Industries
Where we have delivered this service.
Manufacturing
OTIF operations agent, quality & CAPA agent, supplier risk agent, reliability & shift agent
Financial Services
Reconciliation agent, compliance monitoring agent, portfolio risk agent
CPG & Pharma
Trade scheme compliance agent, distributor performance agent, stock-out prediction agent
Good fit
When this service is the right answer
- Work that requires continuous monitoring but cannot justify a dedicated analyst
- Operations, quality, supplier, finance, or customer beats with repeatable signals
- Teams that want AI capacity with explicit human control over consequential actions
Honest call
When this is not the right choice
- You want a generic AI assistant your team can chat with — that is a Copilot, not an agent
- The beat has no repeatable data signals — an agent needs a typed schema to reason over
- Your team is not ready to define what "good" looks like for decisions the agent will prepare
Start the engagement
Start with the beat that needs constant attention but cannot justify a hire.
We scope the first project around that problem, deliver a working result in weeks, and stay on as an operations partner — because the platform keeps getting sharper after go-live.