AI Services
AI Agent Development Services for Autonomous, Auditable Work
We build AI agents that plan multi-step tasks, use your tools and data, collaborate with other agents and ask a person for approval before anything important happens.
Overview
Why AI Agent Development matters
A chatbot answers questions; an agent gets work done. Our AI agent development services create software agents that break a goal into steps, call APIs, query databases, browse the web, write up their findings and hand the result to the right person. Many teams have analysts and coordinators spending hours each week gathering data from five systems and pasting it into a report. An agent can do the gathering and drafting, leaving people to review, decide and act.
We build with CrewAI, LangChain and similar frameworks, wrap every agent in human-in-the-loop controls, and record each decision and tool call in an audit trail. The result is automation you can trust in front of finance, legal or compliance teams, because every output can be traced back to its sources.
What we build
AI Agent Development: capabilities
Every feature is built on our standardized stack, so it is secure, tested and maintainable by any engineer in our pool.
Autonomous multi-step execution
Agents decompose a goal into tasks, carry them out in order, check intermediate results and retry or change approach when a step fails.
Tool use and function calling
Give agents controlled access to REST APIs, SQL databases, spreadsheets, internal services and web search through typed, permission-checked functions.
Long-horizon planning
Handle jobs that span dozens of steps or several hours, with checkpoints so a long research or reconciliation task can resume instead of restarting.
Multi-agent orchestration
Coordinate specialist agents, such as a researcher, an analyst and a reviewer, that pass work between them under a supervising workflow.
Human-in-the-loop controls
Require approval before an agent sends an email, updates a record or spends money, and let reviewers edit drafts before they go out.
Memory and state
Persist task state and relevant history in Postgres or Redis so agents remember prior work, user preferences and open items across sessions.
Observability and audit trails
Log every prompt, tool call, data source and output with timestamps, giving you a replayable record for debugging, cost tracking and compliance review.
Google Workspace automation
Let agents read Gmail threads, file outputs in Google Drive, draft replies for approval and schedule follow-up meetings in Google Calendar.
Key benefits
Outcomes your business can count on
Hours of manual research removed
Repetitive collection and summarizing work moves to agents, so analysts start their day with a draft instead of a blank spreadsheet.
Decisions stay with people
Approval gates mean the agent proposes and a person disposes, which keeps accountability clear for regulated or high-value actions.
Every output is explainable
Because each step is logged with its sources, reviewers can see exactly why an agent reached a conclusion and correct it quickly.
Scales without extra headcount
The same agent workflow can process ten or ten thousand items, running in parallel on stateless workers backed by job queues.
Built to evolve
New tools, models or agent roles plug into the orchestration layer, so the system grows with your processes instead of being rebuilt.
Use cases
Where this delivers value
Competitive intelligence
Monitor competitor websites, pricing pages, press releases and job postings, then deliver a weekly digest of meaningful changes to the leadership team.
Financial report preparation
Pull figures from accounting and banking exports, reconcile them, flag variances and draft the commentary for month-end management reports.
Lead enrichment
Research new leads across public sources, fill in company size, sector and decision-makers, and write the enriched record back to the CRM.
Code review assistance
Review pull requests against your coding standards, highlight risky changes and missing tests, and leave suggestions for the human reviewer to accept or reject.
Contract analysis
Extract parties, dates, renewal terms and liability clauses from contracts, compare them with your standard positions and mark deviations for legal review.
Compliance monitoring
Check transactions, documents or system logs against policy rules on a schedule and open a case with supporting evidence when something looks wrong.
Technology used
Built on one proven stack
Agents are built in Python with CrewAI, LangChain or AutoGen, connected to tools through MCP and typed function definitions, and orchestrated with queues so long jobs run reliably. Results surface in dashboards built on our standard NestJS and Next.js stack.
- CrewAI
- LangChain
- AutoGen
- Model Context Protocol (MCP)
- OpenAI GPT and Claude
- Llama and DeepSeek
- Python and FastAPI
- PostgreSQL
- Redis and BullMQ
- Docker and Kubernetes
Our process
From first call to confident launch
- 01
Discovery
We shadow the people who do the work today, map each step, data source and decision point, and agree which actions an agent may take alone and which need approval.
- 02
Design
We define agent roles, the tools each can call, permission boundaries, memory strategy and the evaluation criteria that decide whether an output is good enough.
- 03
Agile Build
Agents are built and tested sprint by sprint on real historical cases, with traces reviewed together so prompts, tools and guardrails improve with every iteration.
- 04
Production + 90-day hypercare
We roll out with approval gates switched on, watch cost, accuracy and failure patterns closely, and relax controls only where the data supports it during 90 days of hypercare.
How our AI agent development services differ from simple automation
Traditional scripts follow a fixed path and break when input changes. An agent reasons about the goal, picks the next action based on what it has learned so far, and can recover from an unexpected result. That flexibility is powerful, but it needs structure, so we combine free-form reasoning with explicit workflows, typed tools and limits on how many steps or how much spend a single run may use.
Where a process is predictable end to end, a deterministic workflow is often cheaper and safer. We will tell you when that is the case and point you towards AI business process automation instead, reserving agents for the tasks that genuinely require judgement.
Safety, permissions and accountability
An agent should never have more access than the person it works for. We give each agent its own service identity, scope its database and API permissions to the records it needs, and route sensitive actions through approval queues. Every run produces a trace that shows the plan, the tools called, the data returned and the final output.
These controls make agents usable in finance, healthcare and legal settings, where reviewers must be able to explain a result to an auditor. They also help with cost: traces show which steps consume the most tokens, so we can optimize prompts or switch models where it makes sense.
- Least-privilege access per agent and per tool
- Configurable approval gates for outbound or financial actions
- Step and budget limits on every run
- Replayable traces for debugging and audits
Agents that work with your existing systems
Agents become most useful when they can read and write the systems your teams already use. We connect them to your CRM, ERP, ticketing tools and Google Workspace, and where those systems need new APIs we build them in NestJS following the same standards as our custom software development work. An agent that enriches leads can feed a CRM AI integration, and one that reconciles invoices can sit alongside your ERP.
Industries served
Proven across industries
FAQ
AI Agent Development: frequently asked questions
Find your first agent use case
Tell us about a task your team repeats every week and we will assess whether an agent, a workflow or a simpler tool is the right fit.
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