AI Agent Development
Stateful, tool-calling agents engineered like production software, with the memory, guardrails, and observability to survive real users, not just a selected demo.
Production-grade agents, not chatbot demos
Most "AI agent" pilots never make it past a demo. They work beautifully on a selected set of test prompts and fall apart the moment a real user asks something unexpected. We treat agent development as a software engineering discipline, not a prompt-engineering trick, which is the only way it survives contact with production traffic.
An agent, in the way we build it, is a stateful system that can reason across multiple steps, call real tools, remember context across sessions, and recover gracefully when a step fails. That definition rules out the single-shot chatbot wrappers that most teams ship and call "agentic."
Every engagement starts by mapping the specific workflow you want automated, then we architect an agent around it using LangGraph orchestration, typed tool contracts, and full observability from day one, so it behaves predictably once it's touching real customers and real data.
Teams that start here often pair it with AI automation, RAG architecture and LangGraph development.
Boards and CFOs increasingly ask for a concrete agent roadmap, not a research experiment. The difference between a credible answer and a slide deck full of buzzwords is engineering discipline.
Why most agent pilots never reach production
Four gaps separate a chatbot demo from a system your team can trust with real workflows.
No memory between conversational turns, so every session starts from zero and multi-day tasks are impossible to resume.
No structured, validated way to call internal APIs, so a malformed function call can silently write bad data to production.
No fallback when a tool call fails or times out, so one flaky dependency takes the whole workflow down with it.
No trace of why the agent made a decision, so debugging a bad outcome means guessing instead of reading a log.
Everything needed to ship a production agent
A single engagement covers architecture through staged rollout, not just a working prototype.
We map the target workflow end to end, alongside your team, and identify every system the agent will need to touch before any code gets written.
A state machine with conditional branching, built for workflows that loop, wait on external systems, or need a human in the loop mid-task.
Every function call is validated against a strict schema before it executes, with idempotency keys on anything that writes data.
Redis for short-term context and a vector store for long-term recall, so a user can resume a multi-day task exactly where they left off.
Prompt-injection screening, human-approval gates on high-risk actions, and automatic circuit breakers when error rates spike.
Structured tracing of every reasoning step, tool call, and token, so any run can be replayed and audited after the fact.
A maximum number of tool calls, wall-clock time, and token spend per task, so a misbehaving loop can't run away with scope.
Launch behind an internal-only flag first, letting your team stress-test the agent before it reaches a real customer.
From kickoff to staged rollout through controlled rollout
A typical engagement runs through planned review cycles with a working demo at the end of each one, so you see real progress instead of waiting until final delivery.
We map the target workflow in detail alongside your team, identify every system the agent will touch, and agree on explicit success metrics before any code is written. Phase review.
We design the state machine, tool contracts, and memory layers, then review the design with your engineers before writing production code. Phase review.
Weekly increments with a working demo at the end of each week, so stakeholders see real progress rather than a black box. Phase review.
Adversarial red-team testing feeds the agent prompt injection attempts and malformed responses, turning any failure into a permanent regression test. Phase review.
Staged rollout behind an internal-only flag, letting your team stress-test real scenarios before general availability. Phase review.
The tools behind every agent we ship
We stay model-agnostic on purpose: the orchestration layer, tool contracts, and memory design are what make an agent reliable, not which LLM sits behind it. Swapping the underlying model or a single tool never means rewriting the agent.
A LangGraph state machine with conditional edges, not a linear chain, so the agent can branch, loop, and wait on a human approval mid-task.
Claude for planning, tool selection, and synthesis, with routing built to be model-agnostic so a better model can slot in without a rewrite.
Redis holds short-term conversational context, a vector store handles long-term recall, so a task can be resumed days later exactly where it left off.
Containerized inference that scales on real traffic, not a single always-on server, deployed on whichever cloud your stack already runs on.
What this looks like in production
Replacing a five-person triage team
For a logistics client, we replaced a five-person triage team with an agent that reads incoming shipment exceptions, cross-references carrier APIs, and either resolves the exception automatically or escalates it with full context attached. In every case, the win wasn't "the AI is smart," it was the workflow being redesigned around what an agent can reliably verify.
What to look for in an AI agent development company
"An agent is not a smarter prompt, it's a state machine with memory, tools, and a recovery plan for when things go wrong."
Every agent is red-teamed before production, deliberately fed prompt injection attempts and malformed inputs. Any failure found becomes a permanent regression test.
You see a working demo at the end of every week of the build, not a black box that surfaces once at final delivery.
Every rollout launches behind an internal-only flag first, so your team stress-tests real scenarios before a customer ever sees it.
