What is the difference between an AI agent and a chatbot?

A chatbot responds to messages. An AI agent takes actions. It can query databases, call APIs, write files, send emails, and chain multiple steps together to complete a goal. The key difference is the ability to use tools and maintain state across steps.

How do you make sure the agent doesn't go off the rails?

We design guardrails at multiple levels: input validation on tool calls, confidence thresholds that trigger human escalation, sandboxed execution environments, and an evaluation harness that tests edge cases before deployment. No agent ships without a defined failure mode for every tool it uses.

Can the agent connect to our existing systems?

Yes. That's the core of the work. During discovery we map your existing APIs, databases, and internal tools and design the tool-call layer accordingly. We handle authentication, rate limits, and error handling for each integration.

What happens after the project ends?

You receive the full codebase with IP transferred, a runbook, and monitoring set up. We offer optional retainer support for ongoing changes, but your team can maintain and extend the agent without us. There are no ongoing license fees to WayFind Labs.

How long does an AI agent build take?

Most agent builds run four to eight weeks from kickoff to production. A single-workflow agent with one or two integrations lands near four weeks. A multi-step agent with several tools, durable memory, and a full evaluation harness lands near eight. We give a firm estimate at the end of the one-week discovery phase, once we have mapped your systems and the actual decision logic the agent has to handle.

Which model do you use to build the agent?

We choose the model after benchmarking candidates on your actual tasks, not by default or by vendor preference. Reasoning-heavy agents that plan multi-step work usually run on a frontier model; high-volume classification or routing steps often run on a smaller, cheaper model behind the same interface. We design against an internal model interface so swapping models later is a configuration change and an evaluation run, not a rewrite.

What does it cost to run an agent in production each month?

Running cost depends on call volume and model choice, and we instrument it from the first deploy so the number is never a surprise. Most agents we build run on inference costs that scale with usage rather than a fixed platform fee, and we use routing and caching to keep the per-task cost low. You own the infrastructure and the model accounts, so you pay providers directly and there is no markup from us.

AI Agent Development

AI agents that work in production.

We build AI agents that connect to your real APIs, databases, and workflows, not demos that look good in a video. Every agent we ship has an evaluation harness, observability instrumentation, and a runbook. Your team can operate it the week after handoff.

The teams we build agents for typically have a manual process that eats 20 to 40 hours per week. They know roughly what they want to automate but haven't been able to find a team that can build it end-to-end. We do the full job: architecture, integrations, evaluation, and deployment.

How we work

Tell us about your automation problem.

What's included.

Every AI agent engagement covers these six areas. Nothing is optional. They all exist because production agents need all of them.

Agent loop design

We map the full reasoning cycle: what the agent perceives, what it decides, and what actions it can take. Every decision point is explicit and testable.

Tool call architecture

We design the function-calling layer: which APIs get exposed, how inputs are validated, what errors are handled gracefully, and how the agent retries on failure.

Memory strategy

Short-term context management within a conversation window, plus long-term memory backed by a vector store or database. We choose the right pattern for your use case.

Evaluation harness

A test suite that covers the agent's decision-making, not just unit tests. We measure task completion rate, hallucination rate, and latency before shipping.

Observability & logging

Every agent run is traced. You can see the full chain of thought, which tools fired, what the inputs and outputs were, and where latency spikes occur.

Production deployment

We deploy to infrastructure you own and control. Runbook, monitoring alerts, and handoff documentation included. Your team can maintain it without us.

What separates a production agent from a demo.

A demo agent works once, on a clean input, with the person who built it watching. A production agent runs unattended, on the inputs real users actually send, and fails safely when something it has never seen arrives. Four decisions account for most of that gap.

The action space is bounded on purpose. An agent that can call any tool in any order is an agent you cannot reason about. We define exactly which tools exist, what arguments each accepts, and which actions require a confirmation step before they run. A read-only query and an irreversible write are never treated the same way. The result is an agent whose worst-case behaviour you can describe in a sentence, which is the property security reviews and operations teams actually ask for.

Every tool has a defined failure mode. APIs time out, return empty results, and rate-limit at the worst moment. We wrap each tool call with input validation, bounded retries, and an explicit path for what the agent does when the call still fails. The agent surfaces uncertainty and hands off to a human rather than inventing a result. An agent that fabricates a confident answer on a failed lookup is worse than one that says it could not complete the task.

Memory is a design choice, not a default. We decide what the agent remembers within a single task, what persists across sessions, and what is deliberately forgotten. Short-term context stays inside the working window; durable facts go to a database or vector store with clear retrieval rules. Most agent failures we are asked to fix trace back to memory that was either too greedy (stale context poisoning new decisions) or too thin (the agent re-asking for things it should already know).

Evaluation happens before deployment, not after complaints. We build a test suite from real tasks and measure task-completion rate, hallucination rate, and latency against a threshold you approve. The agent does not ship until those numbers clear the bar, and the same suite runs on every prompt or model change so a later tweak cannot quietly regress behaviour. You get the numbers in the handoff package, so you know what the agent does rather than hoping it works.

What clients use agents for.

These are not hypotheticals. Each represents a category of agent we have scoped or built.

Internal knowledge assistant

An agent that answers employee questions by querying your documentation, internal wikis, and databases, with citations so users can verify the answer.

Customer support triage agent

Handles tier-1 support queries end-to-end, escalates to a human when confidence is low, and logs every interaction for QA review.

Sales research agent

Pulls company information, news, and LinkedIn data to brief your sales reps before a call, reducing prep time from 30 minutes to under five.

Workflow automation agent

Monitors a trigger (email, webhook, database change) and executes a defined multi-step workflow (approvals, data transforms, notifications) without human intervention.

Document processing agent

Reads inbound documents, extracts the fields that matter into your system of record, flags low-confidence cases for human review, and posts clean records instead of guesses.

Operations copilot

Sits inside the tool your team already uses and answers operational questions, drafts the routine reply, and takes the next action on approval, with every step logged for audit.

Tech we use.

We choose tools based on your requirements, not vendor relationships. We benchmark before we commit.

ModelsOpenAI GPT-5, Anthropic Claude Sonnet 5, Google Gemini

FrameworksLangChain, LangGraph, custom agent loops

Vector storesPinecone, Weaviate, pgvector, Chroma

Tool callingFunction calling, structured outputs, JSON schema validation

Who this isn't for.

We turn down projects that aren't a good fit. It saves everyone time.

Pre-revenue startups without development budget
We have a minimum engagement size. If that requires raising a round first, this isn't the right time. Come back when you have customers and a budget.
Teams that want a ChatGPT wrapper
If the problem is “give our users a chat box that calls the OpenAI API”, you don't need us. You need a developer for a day. We work on problems that require real tool integration, memory, and evaluation.
Anyone who needs something in 48 hours
A production agent takes at least four weeks. Discovery, architecture, integration testing, eval, and deployment cannot be compressed into two days without cutting the things that keep it from breaking in production.

Inheriting a stalled agent?

A fair share of the agent work we take on is not a new build. It is an agent that was built, demoed well, and then broke once real users touched it: it loops, it calls the wrong tool, it hallucinates an answer when a lookup fails, or it costs ten times what anyone budgeted. The team that built it has moved on, and nobody can say with confidence what it does on an input it has not seen.

We start those engagements with an audit: we instrument the existing agent, replay real traffic against it, and produce a written diagnosis of where it fails and why. Sometimes the fix is a bounded retry layer and a confidence threshold. Sometimes the memory design is poisoning decisions and needs rebuilding. Occasionally the honest answer is that the original approach cannot get to production and we tell you that, with the reasoning, before you spend more on it.

Either way you leave the audit knowing what you have. If you want us to do the repair, the diagnosis rolls into the build scope. If you want to hand it to your own team, the write-up is yours to act on.

Frequently asked questions.

What is the difference between an AI agent and a chatbot?: A chatbot responds to messages. An AI agent takes actions. It can query databases, call APIs, write files, send emails, and chain multiple steps together to complete a goal. The key difference is the ability to use tools and maintain state across steps.
How do you make sure the agent doesn't go off the rails?: We design guardrails at multiple levels: input validation on tool calls, confidence thresholds that trigger human escalation, sandboxed execution environments, and an evaluation harness that tests edge cases before deployment. No agent ships without a defined failure mode for every tool it uses.
Can the agent connect to our existing systems?: Yes. That's the core of the work. During discovery we map your existing APIs, databases, and internal tools and design the tool-call layer accordingly. We handle authentication, rate limits, and error handling for each integration.
What happens after the project ends?: You receive the full codebase with IP transferred, a runbook, and monitoring set up. We offer optional retainer support for ongoing changes, but your team can maintain and extend the agent without us. There are no ongoing license fees to WayFind Labs.
How long does an AI agent build take?: Most agent builds run four to eight weeks from kickoff to production. A single-workflow agent with one or two integrations lands near four weeks. A multi-step agent with several tools, durable memory, and a full evaluation harness lands near eight. We give a firm estimate at the end of the one-week discovery phase, once we have mapped your systems and the actual decision logic the agent has to handle.
Which model do you use to build the agent?: We choose the model after benchmarking candidates on your actual tasks, not by default or by vendor preference. Reasoning-heavy agents that plan multi-step work usually run on a frontier model; high-volume classification or routing steps often run on a smaller, cheaper model behind the same interface. We design against an internal model interface so swapping models later is a configuration change and an evaluation run, not a rewrite.
What does it cost to run an agent in production each month?: Running cost depends on call volume and model choice, and we instrument it from the first deploy so the number is never a surprise. Most agents we build run on inference costs that scale with usage rather than a fixed platform fee, and we use routing and caching to keep the per-task cost low. You own the infrastructure and the model accounts, so you pay providers directly and there is no markup from us.

Ready to scope your agent?

Describe the process you want to automate. We'll reply within one business day with a rough scope and price range, no commitment required.