AI Agent Development Services
Agents that call real systems, get evaluated against a defined bar, and survive contact with production. Built by nearshore engineers who have shipped them.
What an AI Agent Actually Is
An agent is not a chatbot with a better prompt, and it is not a search box over your documents. A chatbot returns text. A retrieval-augmented search box returns text grounded in your content. An agent takes actions: it decides which tool to call, calls it, reads what came back, and decides what to do next, repeating until the task is done or it gives up.
Anthropic's tool use documentation describes the mechanism plainly: the model responds with a tool_use block, your application executes the operation, and you send back a tool_result so the model can continue. Everything else marketed as "agentic" is a variation on that loop.
The distinction matters commercially, because it changes what can go wrong. A chatbot that is wrong produces a bad sentence. An agent that is wrong issues a refund, closes a ticket, or writes to a system of record. That is the entire engineering problem, and it is why so many AI pilots stall at exactly the moment someone asks what happens when it is wrong at scale.
Agent Architectures We Build
Most production agents are assembled from five parts. How many you need is a design decision, not a maturity ladder; plenty of valuable agents use two.
- Tool use. The agent's permitted verbs: query a database, call an internal API, file a ticket, send a document for signature. The tool surface defines the blast radius.
- Retrieval. Grounding answers in your content rather than model memory, with source attribution so a reviewer can check the claim. This is where most enterprise value actually sits.
- Orchestration. Routing between steps, models and sub-agents, including when to stop, when to escalate, and when to hand back to a human.
- Memory. Carrying state across steps and sessions. Useful, and also the part teams most often over-build before they have proven anything.
- Multi-step planning. Decomposing a goal into ordered steps. Powerful, and the hardest component to evaluate, because failure is silent and compounding.
We build these with AI engineers and machine learning engineers working in your stack, alongside our broader generative AI development services.
Guardrails, Evaluation and Testing
An agent without an evaluation set is not a product, it is an anecdote. The single largest difference between teams whose agents reach production and teams whose agents do not is whether anyone wrote down, in advance, what "working" means.
We build three layers, in this order:
- An evaluation set before the build. Real inputs with known-good outputs, drawn from your actual workload, including the awkward cases. It is graded on every change, so a prompt tweak that fixes one thing and breaks four is visible the same day.
- Runtime guardrails. Constraints applied while the agent runs. Amazon Bedrock Guardrails, for example, provides content filters, denied topics, word filters, sensitive information filters that block or mask PII, and contextual grounding checks that flag responses not grounded in the retrieved source, the hallucination case in retrieval-augmented applications.
- Human-in-the-loop review. For any action that is expensive to reverse, a person approves before it takes effect. This is not a failure of ambition; it is how the first production version earns the trust to eventually drop the checkpoint.
We treat agent evaluation as a testing discipline rather than a research activity, the same instinct behind the mistakes we see repeated across AI programmes.
From Pilot to Production
Pilots stall for reasons that are organisational at least as often as technical. A demo needs one good path; production needs an owner, a budget line, an escalation route, and someone accountable when it is wrong at 2am. We sequence the work so those questions are answered while they are still cheap to answer.
1. Use case selection and evaluation criteria
We pick a use case where the agent's output is checkable and the value is countable, and we write the acceptance bar before writing code. If we cannot define what good looks like, that is a finding, and it is better found in week one. Turning a demo into an owned system starts here.
2. Data and integration readiness
Most agent projects are data projects wearing a costume. Retrieval quality is bounded by how your content is structured, permissioned and kept current, which is why this phase frequently runs longer than the model work. Our data engineering teams handle the pipelines and governance that agent retrieval depends on.
3. Thin vertical build
One workflow, end to end, with real integrations and the guardrails already in place. Narrow and real beats broad and simulated, because only real integrations surface the permission, latency and edge-case problems that decide whether this ships.
4. Production hardening and monitoring
Logging every tool call, tracking quality against the evaluation set over time, watching inference spend, and defining rollback. Agents drift as your data, your models and your users change, so monitoring is a permanent cost rather than a launch task. If the surrounding systems are the real constraint, that becomes an application modernization conversation instead.
The structural version of this argument is in our writing on enterprise AI adoption challenges and on standing up an AI center of excellence that ships systems rather than scheduling reviews.
Security, Governance and Data Handling
Agents widen the attack surface because they hold credentials and take actions. The OWASP Top 10 for LLM Applications 2025 ranks Prompt Injection as LLM01 and Excessive Agency as LLM06, and also names Sensitive Information Disclosure, Supply Chain, and Improper Output Handling. Each is addressed in architecture, not in prompt wording.
In practice that means least-privilege tool scoping, treating retrieved content as untrusted input, validating anything the agent emits before a downstream system consumes it, and approval gates on irreversible actions.
For governance we work to the NIST AI Risk Management Framework, released in January 2023 for voluntary use, whose four core functions are GOVERN, MAP, MEASURE and MANAGE. It gives regulated clients a vocabulary their risk committee already recognises. We work under the compliance constraints our financial services and healthcare clients already carry.
Related reading: AI governance evidence you can pull on demand and what executives need on explainability before they scale.
What Drives the Cost
We will not quote a number on a web page, because anyone who does is quoting a different project than yours. What we can do is name the variables that actually move the total, so you can estimate which ones apply.
- Tool and integration surface. The dominant factor. Each system the agent touches brings authentication, permissions, rate limits, error handling and a test environment. Two integrations and eleven integrations are not the same project.
- Data readiness. Whether your content is structured, permissioned and current, or whether that has to be built first.
- Evaluation and guardrail work. Scales with consequence. An internal drafting assistant and a customer-facing agent that moves money need very different amounts of it.
- Human-in-the-loop review. An operational cost, not just a build cost, and one to plan for deliberately.
- Model and inference cost. Recurring and usage-linked. Providers publish current prices, and they change; we size against your projected volume rather than a headline rate.
- Monitoring and ongoing maintenance. Permanent. Budget for it or the agent quietly degrades.
How We Staff Agent Work
Agent projects fail on ordinary engineering discipline far more often than on model selection, so we staff them with engineers, not demo-builders. We hire fewer than 2% of applicants, and every engineer clears four gates before meeting a client: a technical assessment, a live coding review, English proficiency, and culture fit.
Teams work from our delivery centers in Mexico, Colombia and Bolivia, in US time zones, as an extension of your team rather than a separate vendor track. Our staff augmentation and nearshore software development models both apply; most agent work needs a mix of AI engineers, data engineers and QA.
Two engagements worth reading in full:
- RadarFirst: an AI chat assistant that converts natural-language incident reports into structured compliance forms, built on AWS Bedrock, SageMaker and LangChain using retrieval-augmented generation and NLP, over 3+ months.
- Harvard Business Publishing TutorBot: a Microsoft Copilot-based assistant over an AWS multi-format ingestion pipeline with OpenSearch and LLM services, where every answer carried source attribution, over 10+ months.
With 16+ years, 2,500+ projects and a 76 Employee NPS, the engineers who start your project are generally the engineers who finish it.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot answers in text. An agent takes actions: it decides which tool to call, calls it, reads the result, and decides what to do next. Anthropic documents that round trip directly: the model returns a tool_use block, your code executes the operation and sends back a tool_result, and the model continues from there. The agent is that loop plus the systems it is permitted to touch.
How long does it take to get an AI agent into production?
That depends on how many systems the agent touches and how much evaluation its risk profile demands, not on which model you pick. Our staffing timeline is the fixed part: Day 1 discovery, three to five engineer profiles between Days 2 and 10, onboarding across Days 11 to 21, so a team is productive in two to four weeks. Build duration after that follows your integration surface and data readiness.
Why do so many AI agent pilots never reach production?
Most pilots are demos, measured by whether they impressed someone in a meeting. Production needs an evaluation set, a rollback path, a named owner, logging, and a cost ceiling. When none of those exist, a pilot cannot be promoted, only repeated. We agree the evaluation criteria before the build starts, so there is a defined bar the agent either clears or does not.
How do you stop an agent from doing something harmful?
By limiting what it can reach and checking what it produces. The OWASP Top 10 for LLM Applications 2025 lists Prompt Injection as LLM01 and Excessive Agency as LLM06, and both are architecture problems rather than prompt-wording problems. We scope tool permissions narrowly, require human approval for irreversible actions, and apply platform controls such as Amazon Bedrock Guardrails for content and sensitive-information filtering.
What drives the cost of an AI agent project?
Five things: how many tools and systems the agent integrates with, how ready your data is, how much evaluation and guardrail work the risk profile requires, whether a human reviews outputs before they take effect, and monitoring once it is live. Inference is a recurring cost that scales with usage rather than a one-time build line. Model choice is rarely the largest factor.
Do you work with our existing cloud and model provider?
Yes. We have built on AWS Bedrock, SageMaker and LangChain for RadarFirst, and on Microsoft Copilot with AWS OpenSearch for Harvard Business Publishing. Unosquare is an AWS Consulting Partner and a Databricks Partner. We staff into the platform you already run and the compliance constraints you already operate under, rather than asking you to migrate first.
Start With One Real Workflow
Bring us the workflow you would actually trust an agent with, and we will tell you what it would take to ship it, including if the answer is that it is not ready yet.
Still deciding how to engage? Compare engagement models before you scope the work.