Single agent with tools
One model, a toolbox, and a loop that decides what to call next.
Use it when
A conversation or task needs judgement about which system to consult and in what order: customer queries, sales qualification, commerce in chat, internal copilots.
The parts, top to bottom
across every level
Hover or tap any part to see what it does. The light shows the order a request moves through.
What happens, in order
- 1User or event
- 2Agent reasons about the goal
- 3Calls a tool (search, CRM, calendar)
- 4Observes result
- 5Repeats until done
- 6Responds or hands off
What we typically build it with
Trade-offs
Simple to build and reason about; loops and cost need guarding; one agent can be overloaded with too many tools, which is when you split into workers.
Further reading
Problems this architecture solves
Generic problem statements with the flow and the outcomes the industry has documented.
Support queues that never shrink
Tier-1 questions consume most of the team's day, answers vary by agent, and customers wait. Generic chatbots deflect badly because they do not know your policies or your systems.
How the system works
- Customer message
- Intent & policy check
- Retrieve from your docs & orders
- Answer or act via tools
- Escalate with context
Outcome: Klarna's assistant handled two thirds of customer chats in its first month and cut resolution time from 11 minutes to under two; Intercom reports Fin resolving 76% of conversations on average. Klarna press release
ArchitectureSales conversations stuck on personal phones
Orders, questions and promotions run through WhatsApp with no history, no automation and no way to see what drove revenue.
How the system works
- Ad or QR opens chat
- AI agent answers from catalogue
- Cart & payment in chat
- Order synced to store
- Campaign & follow-up
Outcome: 53% of retailers already automate messaging on the WhatsApp Business Platform (Infobip), and the same grounded-agent pattern turns that channel into a store, a support desk and a campaign tool with attribution. Infobip WhatsApp statistics
ArchitectureHiring that stalls on screening and scheduling
High-volume roles generate thousands of applications; recruiters spend their time on screening questions and calendar tennis instead of on candidates.
How the system works
- Candidate applies via chat
- Assistant screens against requirements
- Books interviews on recruiter calendars
- Generates offer & onboarding docs
- ATS updated throughout
Outcome: Conversational recruiting assistants report 40,000 hours saved per week at one retailer, 58% faster application flows and 54% lower cost per hire. Paradox customer results
ArchitectureLeads that go cold before anyone replies
Inbound leads wait hours, reps spend their time on CRM hygiene, and nobody knows which deals deserve attention this week.
How the system works
- Lead arrives
- Enrich from web & CRM
- Qualify & score
- Reply or book meeting
- Log to CRM & alert rep
Outcome: AI-agent adoption in customer-facing service organisations rose from 39% to 66% between 2025 and 2026; the same grounded-agent pattern applies to first response and qualification in sales. Salesforce research via Agentforce ecosystem guide
ArchitectureOther agentic patterns
Supervisor multi-agent system
An orchestrator plans; specialised workers execute; results are merged.
Use it when: Multi-step processes that touch several systems or skills: research and due diligence, back-office case handling, candidate pipelines, anything where one agent's context would overflow.
Flow
- 1Task arrives
- 2Supervisor decomposes into steps
- 3Workers run in parallel with their own tools
- 4Results validated & merged
- 5Human checkpoint if needed
Computer-use agent for software without an API
A perceive-decide-act loop that drives legacy desktops and web apps through the screen, with approval gates.
Use it when: Systems that have no API or export: legacy ERPs, government portals, partner sites, thick-client tools. Use APIs and MCP wherever they exist; use the screen only where they do not.
Flow
- 1Task and policy defined
- 2Screenshot captured
- 3Vision model reads the screen
- 4Click / type / scroll action emitted
- 5Sandboxed execution & verification
Let's build intelligent systems that drive growth
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