AI agent development services for workflows that run in production

How we build agents that survive production

An agent survives production when four things are true: every tool has a typed contract with narrow permissions, state is persisted outside the model's context so a restart does not lose the job, risky writes pause for a human approval, and every step and tool call is logged so a failure can be traced in minutes.

Tool contracts come first. A tool is a function with a schema, a scope and a separate read or write permission. The model fills the schema; it never composes a raw query or an arbitrary HTTP call. Narrow tools make the agent more reliable as well as safer, because the model has fewer ways to be wrong.

State lives in PostgreSQL or Redis, keyed by job, so a long-running workflow picks up where it left off after a deploy or a crash. Before the agent writes to a system of record, the proposed change is shown to a person with the evidence behind it. On the enterprise knowledge platform we built, every AI edit went through that step before it reached the knowledge base, and it is the feature clients mention most.

Traces close the loop. Each run logs the plan, every tool call with its inputs and outputs, and the approval decision, so when someone asks why the agent did something on Tuesday, the answer takes minutes rather than a day of reading transcripts.

What AI agent development costs and how long it takes

At Tecifo, AI agent development starts with a discovery sprint from $2,500, which produces the written plan and an estimate, and a fixed-scope build starts from $6,000. A typical first agent is planned in one to two weeks, built over four to six weeks and launched on one workflow in the week or two after.

Those durations are typical ranges for an illustrative scope, the kind shown in the builder on our homepage; the written plan sets yours. For scale, the agent platform we built for Yazr AI ran to around 800 hours, because it combined multi-agent graphs, structured tool calling, persistent state and a real-time voice pipeline. A single support agent that drafts replies from your help docs is a much smaller job.

What moves the price is the number of tools, whether the agent writes to systems or only reads, how much of your data has to be made retrievable first, and how many workflows you want live at launch. The plan prices each of those, and you can cut any of them before the build is quoted.

How to choose between LangGraph and a Claude API tool loop

Use LangGraph when the workflow has branches, retries and state worth saving between steps, or when several agents hand work to each other. Use a plain Claude API tool-calling loop when one agent calls a handful of tools in a mostly linear order. We default to the simpler option and move to a graph only when the map demands it.

The model choice is separate from the framework choice. We build with Claude, OpenAI and Gemini, and can run Claude through AWS Bedrock when model calls must stay inside your AWS account. Tool-calling quality, cost per run, latency and context length differ per task, so the written plan names a model per step rather than one model for everything.

When an agent is the wrong tool

An agent is the wrong tool when the steps are already known and never change. A fixed pipeline with one model call per step is cheaper, faster and easier to test than an agent deciding the route each time. Agents earn their cost when the path depends on what the data says.

We say this on the architecture call when it applies. Document conversion, scheduled reporting and most classification jobs are pipelines, and we build them as pipelines, often as background jobs behind a queue. A support agent that has to read a ticket, decide what to look up and draft a reply is a real agent, and that is where the tool contracts and approval steps pay for themselves.