Agent strategy and discovery
Map the task, users, data sources, and systems involved. Define where an agent can help, where a simpler automation is enough, and what a useful first pilot should deliver.
AI agent development.
Give your team an AI agent that can find the right information, work with your tools, and move a task forward—with clear boundaries and human review built in.
From a focused pilot to an agent inside your product.
Customer request
“Can you help me change my delivery address?”
- Find the right context
Read the order and approved delivery policy.
- Prepare the next step
Draft a response and a proposed address update.
- A person reviews the change
Apply the update only after the required approval.
Your sources. Defined tools. A clear handoff.
Built around the job your agent needs to do.
Formics combines product design and software engineering with AI integration. We scope the workflow first, then build the knowledge, tools, and review steps it needs.
Custom AI agent development
Build an agent around a defined job, with task instructions, tool access, and clear completion criteria. Shape the interface for the people who will use and review its work.
Knowledge and retrieval
Connect approved documents and business information so the agent can retrieve relevant context. Include source references and a handoff when the available information cannot support an answer.
Business tool integrations
Connect APIs, CRMs, support tools, and internal systems. Give the agent only the actions it needs, with validation and approval steps for changes that require a person’s decision.
Coordinated agent workflows
For work that benefits from separate roles, coordinate research, drafting, and review steps. Define how context moves between them and how failures reach a human operator.
Evaluation and improvement
Test against representative tasks, missing information, and tool failures. Review task completion, answer quality, response time, and running costs as the workflow evolves.
Start with a useful, specific workflow.
Choose a task with clear inputs, accessible information, and an outcome your team can review. These are examples to explore during discovery.
Customer support
Find context. Prepare a useful response.
Bring together help articles and permitted customer records to draft replies, suggest next steps, and route exceptions to your support team.
Internal knowledge
Make company information easier to use.
Help teams search approved policies, product documents, and internal guides, with source references and a clear response when information is missing.
Operations and documents
Move information between everyday tools.
Extract details from incoming documents, check required fields, prepare updates, and send exceptions to a person before the next step.
Sales and research
Turn scattered inputs into a reviewable draft.
Organize research, summarize account information, and prepare follow-ups using approved sources and your team’s review process.
A clear path from idea to pilot.
Agree on the task and how to judge it before expanding the scope. Each stage gives your team something concrete to review.
Define the job
Choose one workflow. Map the people, data, tools, and decisions involved, then agree on what a successful result looks like.
Build a focused pilot
Connect the essential sources and tools. Test the interaction and approval flow with representative tasks.
Evaluate and refine
Review useful answers, incorrect actions, missing context, handoffs, latency, and running costs against the agreed criteria.
Launch and improve
Roll out the agreed scope, document how it works, and plan monitoring, maintenance, and the next set of improvements.
Useful autonomy.
Clear boundaries.
An agent needs more than a prompt. We design how it gets context, which actions it can take, and when your team takes over.
- Access with a purpose
- Define which sources and tools the agent can use, and separate permission to read information from permission to change it.
- A person at the right moment
- Build approval and handoff steps into actions that need review, with enough context for someone to make the decision.
- Answers with context
- Make sources visible where retrieval is used. Define what happens when information is unavailable, conflicting, or out of date.
- Work you can evaluate
- Capture the task path and tool results needed to investigate failures and improve the workflow, within agreed data retention rules.
The right tools for your workflow.
We choose the stack around your systems, data requirements, and budget. A pilot helps establish the tradeoffs before a larger build.
Model providers
OpenAI
Claude
Gemini
Selected around the task, quality requirements, data handling, and usage cost.
Workflows and engineering
LangChain
n8n
Python
Node.js
A workflow builder, custom backend, or a combination, depending on the integration.
Your data and tools
Supabase
Connected to approved documents, databases, business software, and APIs. The architecture follows your existing systems.
Related design work.
Explore reservation and content experiences from our design portfolio.
Reservation workflow design
Booking Agent Bot
Interface design for finding availability, making restaurant reservations, and managing bookings.
AI content interface design
Inspirational Verse
A mobile interface for prompt-based quote creation, content discovery, customization, and sharing.
Before you build an AI agent
Practical questions about scope, integrations, and ongoing costs.
What is the difference between an AI agent and a chatbot?
A chatbot is usually centered on a conversation. An AI agent can also use tools and follow several steps toward a defined task, such as finding an order, preparing a response, and requesting approval for an update. The useful distinction is what the system is allowed to do, rather than the label attached to it.
Should we start with one agent or a multi-agent system?
Start with the simplest setup that can complete the task. A single agent or a fixed workflow may be enough. Multiple agents are worth considering when separate responsibilities or review stages improve the result enough to justify the extra complexity, cost, and coordination.
Can an agent work with our existing software?
Integration depends on the APIs, permissions, and data access your software provides. During discovery, we review your CRM, support tools, databases, and internal systems, then agree which information and actions the agent can access.
How do you handle private data and human approval?
We plan the data sources, access permissions, model provider settings, and retention requirements with your team. The workflow can restrict tool access, separate read and write actions, and require human approval before agreed changes are made. Requirements are scoped for your systems and use case.
How do we know whether the agent is working well?
Before the pilot, we agree on representative tasks and acceptance criteria. Evaluation can include task completion, source accuracy, appropriate handoffs, tool failures, response time, and cost per task. Results guide what needs to change and whether the workflow is ready for a wider rollout.
What affects the cost and timeline?
Scope depends on the task, data preparation, number of integrations, interface, evaluation needs, and deployment requirements. We scope a focused pilot first. Model usage, hosting, and third-party subscriptions are running costs to consider alongside development and ongoing support.
Start with one workflow.
Tell us which task takes up your team’s time, the tools you use, and what a good result would look like. We’ll discuss the scope of a first pilot.