Demos that break in production
A simple tool-calling loop looks great in a video, then fails on edge cases, restarts and ambiguous instructions.
AI Agent Development
AI agent development for real business workflows
We build AI agents that do real work: query databases, call APIs, manage cloud resources, research and draft. Every agent ships with explicit state, human approval for sensitive actions, and evaluations that prove it behaves the way you expect.
What you can expect
- Hours of manual operations handled by agents
- Human approval before any sensitive action
- Persistent memory across sessions and restarts
(01) The problem
Why teams come to us for AI Agent Development.
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No control over actions
Teams are rightly nervous about giving a language model write access to infrastructure, finances or customer data.
Integration sprawl
Every new tool means hand-written schemas, fragile prompts and another round of regression testing.
Black-box behavior
Without traces and evaluations, nobody can explain why an agent did what it did, or prove it won't do it again.
(02) Use cases
What we build with AI Agent Development.
- 01
Cloud and IT operations agents
Create, update and inspect AWS resources in plain English, with confirmation before any change. Built on Amazon Bedrock.
- 02
Research and analyst agents
Multi-agent crews that read transcripts, filings and reports and produce client-ready briefs.
- 03
MCP tool platforms
Model Context Protocol clients and servers that give agents governed access to search, databases and internal APIs.
- 04
Back-office automation
Agents that triage email, update CRMs, reconcile records and route exceptions to the right person.
- 05
Customer-facing agents
Support and sales agents grounded in your knowledge base, with safe escalation paths.
- 06
Content and marketing copilots
Human-in-the-loop drafting agents where publishing always stays a deliberate human step.
Deliverables
Everything you need to run it in production.
- Agent architecture with explicit, checkpointed state
- Tool and API integrations through MCP or function calling
- Persistent memory across sessions
- Human-in-the-loop approval and interrupt flows
- Guardrails, scoped permissions and audit trails
- Tracing and evaluation of agent decisions
- Deployment, monitoring and runbooks
Our approach
How we deliver reliable results.
- 1
Map the real workflow
We document the decisions, tools and approvals a skilled person uses today before automating any of them.
- 2
Choose the right autonomy
A single agent, a multi-agent crew or a deterministic workflow: we pick the simplest design that meets the requirement.
- 3
Make control a feature
Approvals are real execution boundaries, implemented as graph interrupts, not polite requests inside a prompt.
- 4
Test the unhappy paths
Rejections, timeouts, bad tool output and restarts are tested as rigorously as the happy path.
- 5
Observe in production
Every run is traced, with cost tracking and alerts, so behavior stays explainable after launch.
Proof
Related case studies, with real numbers.
Enterprise AI & finance
MCP Multi-Tool Agent Platform
A governed agent platform with persistent memory and human approval before sensitive actions
- pluggable tool access
- MCP
- pluggable tool access
- sensitive calls human-approved
- 100%
- sensitive calls human-approved
- memory across restarts
- Persistent
- memory across restarts
Enterprise IT & cloud
Conversational AWS Infrastructure Agent
Natural-language cloud operations on Amazon Bedrock, with confirmation before every change
- CLI commands required
- 0
- CLI commands required
- changes confirmed before execution
- 100%
- changes confirmed before execution
- enterprise model access
- Bedrock
- enterprise model access
Finance & investment
Multi-Agent Financial Document Intelligence
Specialized AI agents that turn earnings calls and financial PDFs into client-ready reports
- specialized CrewAI roles
- Multi-agent
- specialized CrewAI roles
- transcript to report
- End to end
- transcript to report
- structured output
- Client-ready
- structured output
Productivity SaaS
Content Copilot
A human-in-the-loop, three-agent copilot for LinkedIn content and job applications
- specialized agents
- 3
- specialized agents
- topic scoring
- Web-grounded
- topic scoring
- human-approved publishing
- 100%
- human-approved publishing
Also delivered
- Mentoring a product team on CrewAI, LangChain and LangGraph
- Amazon Bedrock integration and user-session management for production applications
Technology
Tools we use for AI Agent Development.
Model-agnostic and cloud-agnostic: we pick the right components for your data, budget and compliance requirements.
- LangGraph
- LangChain
- CrewAI
- Claude Agent SDK
- OpenAI Agents SDK
- Model Context Protocol
- Amazon Bedrock
- Claude
- GPT-4o
- Python
- FastAPI
- PostgreSQL
Industries we serve
Engagement models
Flexible ways to work, one standard of delivery.
Start small with a discovery sprint or an audit, or go straight to a production build. Most projects start from $5,000, scoped after a free strategy call.
1–2 weeks · fixed scope
AI Strategy & Discovery Sprint
Teams validating an AI opportunity before committing a budget.
- Use-case and ROI workshop
- Data and systems audit
- Architecture blueprint
- Risk and compliance review
- Build plan with estimates
4–12 weeks · milestone-based
Production Build
A defined RAG platform, AI agent, automation or AI product, delivered end to end.
- Architecture and full build
- Evaluation suite and QA
- Security and compliance controls
- Deployment and monitoring
- Documentation and handover
Monthly retainer
Dedicated AI Engineering
Product and platform teams with an ongoing AI roadmap and no time to hire.
- Senior AI engineering capacity
- Weekly planning and demos
- Continuous evaluation and monitoring
- Model and cost optimization
- Scale up with vetted specialists
1–3 weeks · fixed scope
AI Audit & Evaluation
An existing LLM, RAG or agent system that underperforms or is changing hands.
- Architecture and code review
- Answer-quality evaluation
- Security, PII and compliance review
- Cost and latency analysis
- Prioritized remediation report
Not sure which model fits? A free 30-minute call is enough to decide.
Book a free strategy callAI Agent Development FAQ
Common questions about AI Agent Development.
Can't find your answer? Ask us on a free strategy call or send a short brief.
What is an AI agent, and how is it different from a chatbot?
A chatbot answers. An agent acts: it plans steps, calls tools and APIs, reads the results and continues until the task is done, while your rules decide what it may do on its own.
Is it safe to let an agent take actions in our systems?
Yes, when control is designed in. We add human approval gates before sensitive actions, scope permissions per tool, log every step and make rollback paths explicit.
LangGraph, CrewAI or a custom framework?
LangGraph when you need explicit state, persistence and approvals; CrewAI for role-based research crews; plain function calling when the workflow is simple. We use the lightest option that meets the requirement.
What is the Model Context Protocol (MCP)?
MCP is an open standard for connecting AI models to tools and data. Building on MCP means new tools plug in without rewriting your agent, and access can be governed in one place.
Which models do your agents use?
Claude, GPT-4o and newer OpenAI models, Gemini, Llama or Mistral, chosen per task on quality, cost, latency and data-residency needs. Amazon Bedrock and Azure OpenAI are supported for enterprise deployments.
How do you know an agent works?
Task-level evaluations: success rate, correct tool use, approval behavior and cost per task, run on realistic scenarios before every release.
How long does it take to build an AI agent?
A scoped single-workflow agent typically takes three to six weeks. Multi-agent platforms with several integrations usually take eight to twelve weeks.
Taking on new projects
Let’sbuild.
Have an AI project in mind? Let’s make it real.
Book a free 30-minute strategy call with our founder. You’ll leave with a clear view of the right architecture, timeline and next step, whether or not we work together.
- Reply within 24 hours
- NDA before you share anything confidential
- You own the code, prompts and documentation
- Hours overlap with US, UK, EU and Gulf teams