AI agents that finish tasks, and stop for a person
We build agents that plan, use your systems and check their work, inside limits you set.
Our own lead agents are a worked example: see how applyd.ai handles inbound leads.
What we build for agent work
Most projects combine two or three of these. We agree the scope and how we will test it before building.
Task agents
One agent, one bounded job: triage a ticket, draft a reply, pull fields from a document. Narrow tools and a small action space keep it reliable.
Multi-agent workflows
Several agents that each own a step, with a clear hand-over between them and a person at the points that matter.
Tool access
Agents that call your APIs and databases with their own least-privilege credentials, never an admin key.
Approval steps
Sending to a customer, changing a record or spending money can wait for a person, with the evidence attached.
Evaluation and tracing
A test set of realistic tasks, scored on every change, and a trace of every run so you can see what the agent did and why.
Cost and step limits
Hard budgets on steps, time and spend per task, so a stuck agent stops instead of looping.
How a production agent is built
A reliable agent is a controlled loop: reason, act, observe, repeat, inside budgets and permissions, with people approving the actions that matter.
The agent loop
Runs for each task until it is done or a limit is hitNot done yet: plan the next step.
Guardrails
Wrapped around every runEvaluate and improve
Before launch and on every changeTrigger
A ticket, email, form or schedule starts the run with the task and the context the agent may see.
- In practice
- Event-driven task intake
- You get
- A logged run with its inputs
Watch an agent work one task
A scripted run of an agent chasing an overdue invoice: each thought, tool call and check, and a pause for a person before it contacts the customer.
- AgentTask arrives
- Upcoming: Plans the work
- Upcoming: Calls crm.lookup_customer
- Upcoming: Calls billing.get_balance
- Upcoming: Reads the credit policy
- Upcoming: Drafts the second reminder
- Upcoming: Guardrail checks pass
- Upcoming: Stops for approval
- Upcoming: Finance lead approves
- Upcoming: Sent and logged
00:00
Task arrives
An overdue invoice in the finance queue starts a run. The agent can see the task and nothing else yet.
- Source
- Finance queue
- Task
- Chase invoice INV-2041 (example)
How we keep agents safe
An agent that can act needs more control than one that can only answer. These are built in, not added later.
- Approval on risky actionsPersonAnything customer-facing, financial or irreversible waits for a named person.
- Evaluation before every releaseTestedA prompt, model or tool change ships only after it passes the task suite.
- Prompt-injection and data-leak testingTestedWe test how the agent behaves when its inputs try to redirect it.
- Switch-off and rollbackPersonYou can pause an agent at any time, and every run can be traced and reversed where the system allows.
Tools we commonly work with
We choose per task and budget. Naming a tool is not a partnership or an endorsement.
- Models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Agent frameworks
- LangGraph
- LangChain
- AutoGen
- CrewAI
- Claude Agent SDK
- OpenAI Agents SDK
- Retrieval
- pgvector
- Qdrant
- Weaviate
- Pinecone
- Evaluation and tracing
- Braintrust
- LangSmith
- Arize Phoenix
- OpenTelemetry
- Runtime
- Python
- TypeScript
- Temporal
- Docker
Common questions about this work
Keep reading related pages
- LLM integration and RAGAnswers grounded in your own documents and systems.
- AI consultingPick the use case and the simplest design before building.
- How the five agents workCapture, qualify, follow up, book and sync, one job each.
- Security overviewTenant isolation, access, audit and how agents are kept in bounds.
Tell us what you want built. We will scope it with you.
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