AI Agents
AI Agent Development Services
A chatbot answers questions. An agent does the job — reads your systems, decides the next step, and takes action with a human in the loop where it counts.
The challenge
Your team burns hours on multi-step work that moves data between systems — triage, enrichment, document processing, routine ops. It is too variable to script and too repetitive to keep doing by hand.
An AI agent is a system that plans and executes multi-step work rather than replying to one message at a time. It reads from your tools, decides what to do next, calls the functions it needs, and either completes the task or escalates it. The difference from a chatbot is not intelligence — it is consequence: an agent writes to your systems, so the engineering that matters is control, not conversation.
We build agents that triage and route incoming work, enrich and qualify records across a CRM and third-party data, process documents end to end, and run recurring operational workflows that currently eat a person's morning. Each one ships with explicit boundaries on what it may touch.
The hard part of agent work is never the model. It is deciding what the agent is allowed to do unsupervised, what needs human approval, and what it must refuse — then proving it behaves that way before it runs against production data. We build that proof in from the first week: every action is scoped, logged, reversible where it can be, and gated where it cannot.
Agents are worth building when a task is repetitive, spans more than one system, and currently requires a person to copy information between tools. If the work is a single lookup, you want retrieval or a chatbot, and we will say so.
Sound familiar?
If any of this rings true, you're in the right place.
Someone on your team spends every morning moving the same data between tools.
You tried an agent demo that worked once and fell apart on real inputs.
You want automation, but you cannot risk software writing to production systems unsupervised.
Your process has too many exceptions to capture in a rigid if-then script.
How we solve it
Your problem, and exactly how we remove it.
The problem
Agent demos work in a video and fail on your real data.
How we solve it
We build against your actual inputs from week one, including the malformed and edge-case ones, and score the agent on a fixed task set before it goes anywhere near production.
The problem
You cannot let software write to production unsupervised.
How we solve it
We scope every tool the agent can call, gate consequential actions behind human approval, and log each step so you can audit exactly what it did and why.
The problem
The process has too many exceptions for a rigid script.
How we solve it
Agents handle variability that breaks if-then automation — and we define explicit escalation paths so genuine exceptions reach a person instead of being guessed at.
The problem
Costs run away once an agent loops.
How we solve it
We cap steps, budget tokens per run, cache aggressively, and add observability on cost and latency so a runaway loop is impossible rather than merely unlikely.
What we deliver
Scope of work
Tech stack for this service
- OpenAI
- Anthropic
- Python
- FastAPI
- Node.js
- PostgreSQL
- Redis
- AWS
Why CodeBaxh
What sets this work apart.
Shipped, not demoed
We have built a production multi-agent pipeline that analyses 500–1,000-page legal contracts with separate agents for processing, retrieval, query understanding, and response.
Control before autonomy
Every action is scoped and logged, and consequential ones are gated behind human approval until the evidence says otherwise.
Evaluated, not vibed
A fixed task set with known-correct outcomes scores the agent before launch and catches regressions after — the step most agent projects skip.
Cost-bounded by design
Step caps, per-run token budgets, and caching mean an agent loop cannot quietly turn into a five-figure API bill.
We tell you when an agent is overkill
Single-step lookups want retrieval, not an agent. Rigid, exception-free processes want a script. We recommend the cheaper thing when it is the right thing.
How we work
How a AI Agents project runs
A calm, visible rhythm from the first call to launch — short loops, weekly demos, and clear updates throughout.
Scope the task
We map the workflow, the systems it touches, and the decisions that must stay with a human.
Tools & guardrails
We define the exact functions the agent may call, the approval gates, and the refusal cases.
Build & evaluate
We build against real inputs and score the agent on a fixed task set, including the edge cases.
Ship with oversight
We launch behind approval gates with full logging, then widen autonomy as the evidence supports it.
Proof
Relevant work.
A real project that shows this service in production.
Fixed-scope, retainer, or staff-augmentation engagements available. See engagement models or book a discovery call.
From the blog
Related guides
FAQ
AI Agents — FAQs
A chatbot answers; an agent acts. A chatbot retrieves information and replies in conversation, so its worst failure is a wrong answer. An agent plans multi-step work and calls tools that change state in your systems, so its worst failure is a wrong action. That difference is why agent projects spend most of their engineering on permissions, approval gates, and audit logging rather than on conversation design.
Six to ten weeks for a production agent against real systems, and longer where approvals are involved. A single-workflow agent with two or three tools and a human approval gate is the common case at the shorter end. What extends it is integration access rather than engineering: every system the agent reads from or writes to needs credentials and sign-off, and those loops routinely cost more calendar time than the build.
Yes, and this is the core of the work rather than an add-on. The agent can only call functions we explicitly give it, so its capabilities are a whitelist rather than a boundary it might cross. Consequential actions sit behind human approval, every step is logged with its inputs and reasoning, and we define refusal cases for anything outside scope.
Hard step caps and per-run token budgets, enforced in code rather than requested in a prompt. An agent that hits its ceiling stops and escalates instead of continuing. We add cost and latency observability per run, so an unusual pattern is visible the day it starts rather than at the end of the billing month.
Usually one, and multi-agent setups are oversold. Splitting into multiple agents helps when roles are genuinely distinct and each needs different tools or context — the way separate processing, retrieval, and response agents work in a document pipeline. Splitting a single coherent task across agents adds coordination failure modes without adding capability, so we start with one and split only where the evidence justifies it.
It escalates rather than guesses, because we build the escalation path before we build the autonomy. Actions are reversible where the underlying system allows it, gated behind approval where it does not, and every step is logged so you can reconstruct what happened. During rollout the agent runs behind approval on everything, and we widen autonomy only where the task-set scores support it.
Yes — anything with an API, and often things without one. Agents connect to CRMs, ticketing systems, databases, internal services, and third-party data providers through tools we define. Where a system has no API, we usually reach it through the database or an integration layer. Mapping this is the first thing we do in discovery, because integration access, not model capability, is what sets the timeline.