AI Agent Development That Ships Past the Demo
An AI agent is software that uses an LLM to plan, call your tools, and complete a multi-step task with limited supervision. MicroPyramid builds custom AI agents, agentic workflows, and multi-agent systems for startups and SMBs — grounded in your data, wired into your systems, and backed by the evaluation, guardrails, and monitoring that most demos skip. Shipped in weeks, with senior AI engineers owning the build.
AI Agent Development Services
From a single task-completing agent to coordinated multi-agent systems — with the grounding, guardrails, and evaluation that make them safe to run
Custom AI Agents
Task-completing agents that plan, call your tools and APIs, and finish multi-step work — not just chat back at the user.
- Goal-driven planning & reasoning
- Tool / function calling
- Human-in-the-loop checkpoints
Agentic Workflow Automation
Replace brittle manual or rules-based processes with agents that read context, decide, and act across your systems.
- Document & ticket triage
- Research and data gathering
- Back-office process automation
Multi-Agent Systems
Coordinated agents that split a complex job into specialised roles — planner, researcher, executor, reviewer.
- Orchestration & routing
- Specialised sub-agents
- Shared memory and state
Tool & System Integration
Wire agents safely into the systems they act on — your APIs, databases, SaaS tools, and internal services.
- API & MCP tool servers
- CRM, ERP & helpdesk hooks
- Scoped, auditable permissions
RAG-Grounded Agents
Agents that retrieve from your own documents and data before they act, so answers and decisions stay grounded in fact.
- Vector search over your data
- Source citations
- Reduced hallucination risk
Evaluation, Guardrails & Monitoring
The part most demos skip — measuring whether the agent is actually correct, safe, and cost-controlled in production.
- Eval suites & test cases
- Guardrails and fallbacks
- Tracing, cost & latency monitoring
Where an Agent Earns Its Keep
If any of these match where you are, an agent is probably worth a conversation
Customer Support Teams
You want an agent that resolves common tickets end-to-end — reading the account, checking systems, and taking action — with escalation to a human when unsure.
Operations & Back Office
You have repetitive multi-step workflows — triage, data entry, reconciliation, research — that rules engines never quite handled and humans find tedious.
Internal Knowledge Work
You need an agent that searches your documents and tools, synthesises an answer with citations, and drafts the next step for a person to approve.
SaaS Teams Adding Agents
You want to ship an in-product copilot or autonomous workflow as a feature, and need engineers who can make it reliable for real users.
Teams With a Failed POC
You built an agent demo that impressed in a meeting but broke on real data, cost too much, or could not be trusted in production.
Lean Teams Without ML Staff
You do not have an in-house AI team and need a senior partner to design, build, evaluate, and hand over a working agent system.
Best Fit For
- teams with a real multi-step task to automate — not just a chatbot that answers FAQs
- startups and SMBs adding an agent or copilot as a product feature or internal tool
- teams that need the agent grounded in their own data, tools, and permissions
- teams that want evaluation, guardrails, and monitoring — not a demo that breaks in production
Not the Right Fit When
- a static FAQ bot with no actions, where a simple RAG assistant is the better fit
- fully autonomous, unsupervised control over high-risk actions with no human checkpoints
- "add AI" as a marketing slogan with no concrete task, data, or workflow behind it
- expectations of 100% accuracy with zero evaluation, oversight, or fallback design
If you need a grounded assistant or doc search rather than actions, see AI / RAG Knowledge Systems, or AI Feature Development to embed one capability in your product.
Custom Agent, Off-the-Shelf Copilot, or No-Code?
The honest version of the trade-off — so you only invest in a custom build when it actually pays off
Off-the-shelf copilot
Generic assistance fast — drafting, summarising, Q&A inside tools you already pay for.
Cannot act inside your systems, no access to your private data or workflows, and you cannot tune accuracy.
Pick when the need is general productivity, not a task specific to your business.
No-code agent builder
A quick first workflow without engineers, useful for prototyping and simple internal automations.
Hits a wall on real integrations, permissions, evaluation, and cost control; hard to debug when it misbehaves.
Pick for low-stakes internal experiments where occasional errors are acceptable.
Custom-built agent (what we do)
Built around your task, grounded in your data, wired into your tools, evaluated, and monitored in production.
Needs engineering investment up front — worth it when the workflow is core, sensitive, or high-volume.
Pick when the agent touches real systems, real data, or real customers and has to be trusted.
How We Build an Agent You Can Trust
Reliability comes from the order of operations — task and evaluation first, autonomy last
Pin Down the Task
We define the specific task, the systems involved, and what "good" looks like — before writing agent code. Most failed agents skipped this.
Prototype the Loop
We build the smallest working agent loop against real data and tools, so you see real behaviour early instead of a scripted demo.
Ground, Integrate & Guard
We add retrieval, tool access with scoped permissions, human checkpoints, and guardrails so the agent is safe to run.
Evaluate & Ship
We measure accuracy and cost against a test suite, add tracing and monitoring, then ship in stages with a human in the loop.
AI Agent Technology Stack
Model-agnostic by design — we pick the model, framework, and data layer that fit your task, budget, and data residency
Models
Orchestration & Retrieval
Engineering & Ops
How to Get Started
We recommend starting with an Agent Discovery Sprint — confirm an agent is the right tool before committing to a full build
Agent Discovery Sprint
Clarify the task, data, tools, and risks, and confirm an agent is the right tool before committing to a build.
- Use-case & feasibility review
- Data and tool inventory
- Architecture & guardrail plan
- Clear delivery roadmap
Agent Pilot Build
Ship one working agent against real data and tools, with evaluation and a human-in-the-loop, ready to trial with users.
- One end-to-end agent
- Real integrations & retrieval
- Eval suite & guardrails
Agent Scale & Operate
Harden a working agent for production and expand it — more tools, more workflows, monitoring and cost control.
- Production hardening
- New tools & workflows
- Monitoring, retainer or T&M
Selected Work
Products we have built and shipped for startups and SMBs, including AI-assisted platforms like Refactored.ai.
Refactored.ai
AI-assisted Python learning platform with interactive tutorials, exercises, and automated assessment
Read case study
PRO Music Tutor
Online music learning platform connecting students with world-class instructors
See portfolio
Bough Digital
UK digital marketing platform with campaign management and analytics
See more work
CREDITABLE
Employee financial wellness platform for savings, loans, and workplace finance
See more workFrequently Asked Questions
Straight answers to what founders and product leaders ask us before building an agent.
What is an AI agent?
An AI agent is software that uses a large language model to plan and complete a multi-step task with limited supervision — it decides what to do, calls tools or APIs to take real actions, observes the result, and continues until the task is done. Unlike a chatbot that only replies with text, an agent can read context, retrieve data, and act inside your systems.
How is an AI agent different from a chatbot or a RAG assistant?
A chatbot answers questions in text; a RAG assistant answers questions grounded in your documents; an AI agent goes further and takes actions — calling tools, updating records, or running a multi-step workflow to actually complete a task. Many real systems combine all three: retrieval to stay grounded, conversation for the interface, and agentic tool-calling to get work done.
When should we build a custom agent instead of using an off-the-shelf copilot?
Use an off-the-shelf copilot for general productivity like drafting and summarising. Build a custom agent when the task is specific to your business, needs access to your private data and systems, must follow your permissions and rules, or has to be trusted in production — things generic copilots and no-code builders cannot do reliably.
How do you stop an AI agent from hallucinating or taking wrong actions?
We ground the agent in your real data with retrieval and citations, scope its tool permissions so it can only do safe things, add human-in-the-loop checkpoints before high-risk actions, and build an evaluation suite that measures accuracy on real cases. Guardrails, fallbacks, and production monitoring catch the rest — this evaluation layer is what separates a reliable agent from a demo.
Which models and frameworks do you use to build agents?
We are model-agnostic and choose per use case — Claude (Anthropic), OpenAI GPT models, or open models like Llama and Mistral where data residency or cost matter. We build with Python and FastAPI, orchestrate with tools like LangGraph, connect tools via the Model Context Protocol (MCP), retrieve with pgvector or Pinecone, and trace and evaluate so the system stays measurable.
How long does it take to build a working AI agent?
A focused agent pilot against real data and tools typically ships in weeks, not months. We start with a short discovery to confirm feasibility, prototype the smallest working agent loop early, then add grounding, integrations, guardrails, and evaluation before a staged production rollout with a human in the loop.
Do we own the agent and the code?
Yes. You own all source code, prompts, evaluation suites, and intellectual property we produce. Everything is committed to your repositories as we build, with no lock-in, so you can run, extend, or bring the work in-house at any time.
Turn a Workflow Into a Working Agent
Bring us a real task — support resolution, back-office automation, research, or an in-product copilot — and we will tell you honestly whether an agent fits, then build one you can trust in production.