Agent architecture and scoping
We map the tasks worth automating, define what the agent may and may not do, and pick the right pattern: single agent, tool-calling loop, or a coordinated multi-agent system.
010AI Agent Development Company
AI agents that do real work in your systems
We design and build AI agents that read your data, call your tools, and complete multi-step tasks without a human driving every click. For CTOs and product leaders who want more than a chatbot: agents wired into your APIs, your permissions, and your workflows, engineered by a senior team that ships to production.
AI005
What we build
AI agent development, end to end
From the first scoping call to a monitored production deployment, we cover every layer an agent needs to be trusted with real work.
Tool and API integration
Agents are only useful when they can act. We connect them to your CRM, databases, internal APIs, and third-party services with typed tool definitions and strict permission boundaries.
Retrieval and memory
We ground agents in your documents, tickets, and product data so answers come from your sources, not the model's guesses, and give them memory that persists across sessions where the workflow needs it.
Conversational and task-driven agents
Customer-facing assistants that resolve requests, and back-office agents that process documents, reconcile records, or triage queues. Both get the same engineering discipline.
Evaluation and guardrails
Before an agent touches production we build test suites for its decisions, add output validation and human approval steps for risky actions, and measure accuracy against real cases.
Deployment, monitoring, and iteration
We ship agents into your infrastructure with logging, tracing, and cost tracking, then tune prompts, tools, and models based on how they behave with real traffic.
How we work
From use case to production agent
(4)- 1
Discovery and feasibility
We study the workflow you want to automate, the systems it touches, and the failure cost of a wrong action, then tell you plainly whether an agent is the right tool.
- 2
Prototype on real data
Within the first weeks you get a working agent running against your actual data and tools, so decisions about scope and model choice rest on evidence, not slides.
- 3
Harden and evaluate
We turn the prototype into production software: permission controls, fallback paths, evaluation suites, and human-in-the-loop checkpoints where the stakes require them.
- 4
Deploy and improve
We release into your environment, watch traces and outcomes, and iterate on prompts, tools, and routing until the agent earns its place in the workflow.
Why Webisoft
An engineering studio, not a demo shop
Plenty of teams can wire a model to an API for a demo. Getting an agent to behave reliably inside a business is a software engineering problem, and that is what we do.
Senior engineers on every project
Your agent is built by experienced software engineers who have shipped production systems, not handed to a junior team after the sales call.
Full-cycle delivery
Strategy, architecture, integration, deployment, and support come from one team, so nothing falls between an AI consultant and a separate dev shop.
Model-agnostic choices
We select models and frameworks based on your accuracy, latency, cost, and privacy constraints, and design the system so you can switch providers later.
Built for your stack
Agents run inside your infrastructure and security model, with audit logs and access controls your compliance team can actually review.
FAQ
AI agent development questions
(4)- A chatbot answers questions in a conversation. An AI agent takes actions: it plans steps, calls tools and APIs, checks its own results, and completes a task such as processing a refund or compiling a report. Chatbots are useful for support and information retrieval, while agents automate operational work end to end. The operational value in most businesses sits with agents because they replace steps in a workflow rather than just answering about it.
- A working prototype on real data typically takes a few weeks. Production hardening adds more time depending on how many systems the agent touches and how costly a mistake would be, since integrations, evaluation suites, and guardrails all need to be built and tested. Simple single-tool agents can ship in one to two months, while agents that act across several core systems often take a quarter. A discovery phase at the start makes the timeline estimate much more precise.
- Agents are built with major model providers and open-weight models, orchestrated by anything from a lightweight tool-calling loop to a full multi-agent framework. The right choice follows the requirements for accuracy, cost, latency, and data privacy rather than vendor preference. Simple workflows rarely need a heavy framework, while complex multi-step tasks benefit from structured orchestration, evaluation tooling, and observability. Model choice is also worth revisiting periodically as capabilities and pricing change quickly.
- Through layered controls: strict tool permissions, output validation, human approval for irreversible actions, and evaluation suites that test the agent's decisions before and after every change. High-risk steps stay behind a human reviewer until data shows the agent handles them reliably. Logging every action the agent takes also matters, so mistakes can be traced and the underlying prompt, tool, or model issue fixed. This defense-in-depth approach is standard practice for agents operating in production systems.
Agent Engineering Capabilities
Where We Add Value in AI Agent Development
The gap between an impressive agent demo and an agent you can trust with real work is engineering: tool design, guardrails, evaluation, and cost control. This is the work we do.
Agent Architecture Selection
We match the architecture to the job: a single tool-using agent for contained tasks, orchestrated multi-agent setups for workflows with distinct roles, or a plain pipeline when no agent is needed at all. Choosing the simplest design that works is the biggest cost and reliability decision in the project, and we make it explicitly with you.Tool and Integration Layer
Agents are only as capable as their tools, so we build typed, permissioned tool interfaces to your CRM, ERP, databases, and internal APIs, increasingly over MCP so the same tools serve multiple agents and clients. Each tool is designed for model consumption with tight schemas, bounded outputs, and clear error messages the agent can recover from.Guardrails and Approvals
We tier every action by risk: reads run freely, routine writes are logged, and consequential actions like sending money or emailing a customer require human approval or hard policy checks. Input and output filtering, spend limits, and step budgets keep an agent from looping expensively or acting outside its mandate.Memory and Context Management
Long-running agents need to remember without drowning in tokens. We implement conversation summarization, retrieval over your documents and past interactions using vector and keyword search, and scoped working memory per task, which keeps behavior consistent across sessions while holding inference costs flat.Evaluation Before Launch
We build an evaluation harness with real task scenarios and score agent runs on outcome correctness, tool usage, and safety, then run it on every prompt or model change. This is the difference between knowing your agent resolves a defined share of cases and hoping it does, and it is where most in-house agent projects skip a step and stall.Production Operations
We ship agents with tracing on every step, token and cost dashboards, latency budgets, and fallbacks when a model provider degrades. Model choice stays flexible, Claude, GPT, Gemini, or a self-hosted model, behind an abstraction so you can switch on price or capability without rewriting the agent.
Our Approach
How an Agent Engagement Runs
(4)- 1
Use Case Qualification
We start by testing whether an agent is actually the right tool, since many candidate workflows are better served by simpler automation. The deliverable is a scoped definition of the agent's job, the tools it needs, the risk tier of each action, and the metric that will define success. - 2
Prototype Against Real Tasks
Within the first weeks we build a working agent against staging data and run it on real historical cases from your operation. This surfaces the failure modes early, ambiguous instructions, missing tools, bad data, and gives your team something concrete to react to instead of a slide deck. - 3
Hardening and Evaluation
We add guardrails, approval flows, error recovery, and security review, and we grow the evaluation suite until it covers the edge cases your team flagged. Exit criteria are agreed numbers, task success rate and escalation rate on the eval set, not a subjective sense that the demo feels good. - 4
Supervised Rollout
The agent goes live on a slice of real volume with human review of its outputs, and autonomy expands as measured performance earns it. We review traces with your team weekly during rollout, then hand over the operational stack or run it under a support agreement, including the retraining and prompt update cycle.
FAQ
Questions Buyers Ask About Agent Projects
(6)- Scope of autonomy and integration surface. An agent that drafts responses for human approval inside one system is weeks of work. An agent that acts autonomously across several systems with compliance constraints takes months, because most of the budget goes to tool integration, guardrails, and evaluation rather than prompting. Ongoing inference cost matters too, and we model per-task token cost during the prototype so there are no surprises at production volume.
- By design, not by trust. Every action the agent can take is enumerated and risk-tiered, high-risk actions require human approval or are blocked outright, and the agent operates under scoped credentials so it physically cannot touch systems outside its mandate. Step limits and spend caps prevent runaway loops, and full traces of every run mean any incident can be reconstructed and the gap closed. We also red-team the agent with adversarial inputs, including prompt injection through the data it reads, before launch.
- We treat both as replaceable parts. Model choice comes down to the accuracy, latency, and cost trade-off measured on your evaluation set, not brand preference, and the answer sometimes differs per step within one agent. On frameworks we keep the footprint light, often plain SDK code with MCP for tools rather than heavy orchestration layers, because the ecosystem shifts quickly and thick abstractions become debt. The architecture lets you swap models when pricing or capability changes.
- Three things: API or database access to the systems the agent will work in, ideally in a staging environment first, examples of the task done well, such as historical tickets with resolutions, and a few hours per week from someone who knows the workflow. Access is granted through scoped service accounts you control and can revoke. If your example data is thin, we design the rollout so the agent generates its own labeled history under human review.
- We define the metrics before the build: task success rate, escalation rate to humans, correction rate on its outputs, cost per completed task, and the business number it is supposed to move, like handle time or backlog age. Production traces feed dashboards for all of these, and a sample of runs gets ongoing human grading so quality drift is caught early. When a model update or prompt change is proposed, it must beat the current version on the evaluation suite before it ships.
- Pick a workflow that is high volume, mostly text or data based, and tolerant of an occasional escalation, such as support triage, internal request handling, or document processing, and defer the fully autonomous customer-facing ideas until you have operational experience. We usually begin with a short qualification phase that produces a scored shortlist of your candidate use cases and a build plan for the best one. Starting supervised and expanding autonomy with evidence is consistently cheaper than starting ambitious and retreating after an incident.
