AI agent development services,
guardrailed for production
Autonomous, tool-using agents that take real actions in your systems — built MCP-native, gated by evals, and held to a human on the high-risk steps.
This is the agent spoke of our AI Development practice. Where Generative AI Development covers apps and RAG that answer, this page is about agents that act. They read a goal, call tools, and carry a task to a result. Already running a product? AI Integration Services covers dropping an agent into it without a rewrite. Not sure where an agent earns its keep? Start with AI agent use cases.
Agents, proven
in production
Tool-using agents that act in real systems, with guardrails and eval gates holding the line. Built by senior engineers and run after launch.
100+
Production software across fintech and logistics, now including agents wired into live tools and APIs.
4–6wks
From a brief to a guardrailed agent running in staging.
100%
Senior engineers on every agent; no juniors learning on your workflows.
99.9%
Across the systems and agents we run after launch.
Put a guardrailed agent into your stack
Tool use, evals, and human-in-the-loop, shipped by senior engineers.
An agent that acts,
and stays in bounds
A chatbot answers. An agent picks up a goal and works it across several steps — which means the tools it holds and the limits around it are the whole job.
Tool use & function calling
The agent calls your functions and APIs as typed tools, so it reads real state and acts on it instead of describing what it would do.
MCP integrations
Your APIs and SaaS surface as MCP servers the agent connects to. Adding a tool means standing up a server, not rewriting the agent.
Memory & planning
Working memory and a planning loop carry context across steps, so a multi-step task survives past a single model call.
Guardrails & human approval
Inputs and outputs are validated, and high-risk actions pause for a person to confirm before they run against production.
An eval harness
A scored suite of agent runs gates every release, so a prompt or tool change that breaks a workflow is caught in CI.
Tracing of every run
Each run records its tool calls and decisions. When an agent does something odd, you read exactly why instead of guessing.
The kinds of agents
we ship
Same guardrailed core, shaped to the job — from a single-task agent to a coordinated multi-agent system. Each one is MCP-native and eval-gated; what changes is the scope it coordinates, not how it is built.
Single-task agents
One job carried end to end against two or three APIs. It is the quickest way to get a working agent in front of real traffic and see how it behaves before you widen its reach.
Multi-agent systems
A planner that hands work to specialist agents and reconciles what they return. When one task is too broad for a single agent, it gets split, run, and merged.
RAG-grounded agents
The agent pulls from your own data before it acts, so its decisions sit on current state rather than a snapshot frozen at training time.
Workflow automation agents
An agent that plans a task across several systems and pauses for a person on the steps that move money or reach a customer. The rest it runs on its own.
Conversational & voice agents
Agents wired into a support chat or voice line that field a request and act on it in the same turn — look up an order, update a record, book a slot — instead of passing it to a human queue.
Embedded product agents
An agent dropped into the product you already run, reached through its own APIs and launched behind a feature flag — so it starts on a narrow slice and earns wider access only as the usage holds up.
From task to a
guarded agent in staging
A six-stage loop in three phases. Senior engineers and AI agents move one task from a mapped goal to a traced, gated agent your team can watch run.
Scope
Map the task
Task & tools mapping
Pin down the one task worth automating and the exact set of tools the agent may call. The boundary is the decision that drives everything after it.
Agent & tool design
Design the planning loop, memory, and the typed tool contracts — and decide upfront which actions a person has to confirm.
Build
Wire & test
Build the agent
Prompts, tool definitions, and MCP connections into your APIs, assembled into an agent that runs the task end to end against staging data.
Eval & red-team
A scored eval suite runs in CI, and we red-team the agent for prompt injection, tool misuse, and the failure modes that only autonomy creates.
Ship
Guard & watch
Guardrails & human-in-the-loop
Output validation and approval gates on the high-risk steps, so the agent acts freely where it is safe and waits for a person where it is not.
Deploy & trace
Ship behind a flag with full run tracing. Every tool call and decision is logged, so you can widen the agent's scope on evidence, not hope.
Agents and AI we
shipped with partners
From an AI-assisted crypto-finance ecosystem to document AI on blockchain and a clinical platform — intelligent systems built to act under production constraints.
Crypto trading platform with wallet, card & academy
An integrated crypto trading platform that puts trading, hardware-wallet custody, a debit card, fiat on- and off-ramps, and an education academy under one account.
eIDAS-qualified e-signature platform with KYC & blockchain audit
One platform to sign agreements, verify identities, and collect payments, all eIDAS-qualified with a blockchain audit trail on every signature.
Medication management software for patients and care teams
Medication management software with two sides, an accessible patient app and a clinical adherence dashboard, joined by bidirectional HL7/FHIR EMR sync.
Field notes on
building agents
Guides and engineering notes on tool use, MCP, evals, and keeping autonomous agents in bounds in production.
- AI & Machine Learning · 20 min · January 7, 2026
Model Context Protocol (MCP) for Developers
- AI & Machine Learning · 19 min · January 5, 2026
Claude Code Skills: Teaching Your AI Coding Agent Your Stack
- AI & Machine Learning · 18 min · January 2, 2026
The AI Software Engineer: What the Role Actually Is Now
- AI & Machine Learning · 18 min · December 31, 2025
AI Pair Programming: How Senior Engineers Work With AI
- AI & Machine Learning · 18 min · December 29, 2025
AI Coding Tools in 2026: The Landscape, Honestly
AI agent development
questions
What product teams ask before they put an autonomous agent in production.
AI agent development is building software that decides and acts on its own across several steps, instead of returning one answer to one prompt. The agent reads a goal, calls tools and APIs to gather state, plans the next step, and carries the task through to a result. A chatbot replies. An agent books the refund, reconciles the ledger, or files the ticket — under guardrails you control.
Cost tracks the number of tools the agent touches and how much oversight each action needs, not a list price. A single-task agent against two or three APIs is a short fixed-scope engagement; a multi-step workflow agent that writes to production systems costs more because the guardrails and evals around it are the real work. We scope to a guarded agent in staging first, price that, then decide what to automate next.
A guarded agent running in staging usually takes 4 to 6 weeks. The first two weeks map the task and the tools the agent is allowed to call. The build phase wires up prompts, tool definitions, and MCP connections, with an eval suite growing alongside. Wiring an agent into one well-documented API is faster; a workflow that spans several systems with human approval steps sits at the upper end.
MCP is the Model Context Protocol, an open standard for exposing tools, data, and actions to a model through one consistent interface. Without it, every integration is a bespoke adapter you maintain by hand. We build MCP-native agents so a new tool is a server the agent connects to, not a rewrite. It is not mandatory, but on anything past a single API it saves real maintenance.
Four layers. Guardrails constrain what the agent can call and validate every input and output. High-risk actions — moving money, deleting records, messaging a customer — route to a person for approval before they execute. An eval suite gates each release, so a prompt change that breaks a workflow is caught in CI. And every run is traced, so when an agent does something odd you can read the exact tool calls and reasoning behind it.
Yes. Most agents we ship live inside a product that already exists, reached through its APIs, with no rewrite of what you run today. We start the agent on a narrow task behind a flag, watch the traces and evals, then widen its scope as it earns trust. That work runs through our [AI Integration Services](/ai-integration-services), so the agent fits your stack instead of replacing it.
Put an agent to
work in your stack
Partner with Idealogic for autonomous agents that act under control — MCP-native, eval-gated, and traced, built by senior engineers.