AI agent development
We build production AI agents for enterprise operations — the kind with explicit state, auditable tool calls, and an evaluation suite proving they work before anyone depends on them.
An AI agent development company builds systems where a language model plans and executes multi-step work against your real tools and data. The engineering that matters is not the model — it is the tool surface, state management, guardrails, and evaluation harness around it. TrueLeaf Tech builds these model-agnostically across Claude, GPT, Gemini, and open-weight models.
What we build
An AI agent earns its place when it finishes a task a person would otherwise do by hand. Everything below is built around that test.
Multi-step processes with explicit state, retries, and human approval gates — order exceptions, claims triage, reconciliation, onboarding checks.
Agents that gather from many sources, cite what they used, and produce a reviewable artefact rather than an unattributed summary.
Extraction, enrichment, and classification at scale, with sampling and review queues so a bad automated edit never propagates unchecked.
Grounded in your documents and systems, with role-based scoping and an audit trail of what was read and why.
Narrow, well-typed tool surfaces over your existing services, so the agent extends your systems instead of duplicating them.
Test sets, tracing, and cost dashboards, so you can see what the agent did, what it cost, and whether it is getting better.
How we work
Most failed agent projects fail the same four ways. We front-load each one.
The single biggest determinant of success is scope of authority. An agent that drafts and asks for approval is a different engineering problem from one that acts unsupervised, and the second costs several times more to make safe. We settle this first, in writing, because it drives architecture, budget, and how much evaluation is enough.
A tool called run_sql is a liability. A tool called get_open_invoices_for_customer is testable, auditable, and hard to misuse. We design the tool surface as an API contract, with types and permissions, so the failure modes are the ones you already understand from ordinary software.
Agents that keep their state in the conversation history are impossible to resume, debug, or audit. We keep state in a store the agent reads and writes deliberately, which means a run can be paused, inspected, replayed, and resumed after a failure — the difference between a demo and something operations can rely on.
An agent that succeeds 85% of the time sounds usable until it runs a thousand times a day. We build the evaluation harness before the feature, run it in CI, and report accuracy and cost per run, so the decision to widen scope is made on evidence rather than optimism.
Engagement
We map the workflow, agree the success measure, and identify the systems the agent must touch. You get a written scope and a realistic cost envelope, including what we would not build.
One workflow, real data, real users, with evaluation and observability from day one. Narrow enough to cancel cheaply if the economics do not hold.
Guardrails, cost controls, runbooks, and documentation. We would rather write ourselves out of a dependency than sell an operating contract you did not ask for.
Model upgrades, evaluation maintenance, and scope extension — but as a choice, not a lock-in. The evaluation suite is yours either way.
In practice
Two engagements where the agent pattern earned its keep, and one where we advised against it.
The manual work was reviewing and correcting product attributes. The agent proposes edits; a sampling queue puts a human in front of a statistically meaningful slice; anything below a confidence threshold is escalated rather than applied. The measurable win was not speed — it was that error rate became visible and controllable for the first time, because every proposed edit carried a traceable justification.
Exception handling is a good agent shape: high volume, well-defined inputs, a bounded set of actions, and a human already in the loop for the hard cases. The agent handles the routine resolutions and routes genuinely ambiguous cases to a dispatcher with the context already assembled, which is where most of the time saving actually comes from.
A client wanted an agent to make pricing changes autonomously. We built the analysis half and left the action half as a human decision with a one-click approval. The reasoning was simple: the cost of a wrong automated price change vastly exceeded the labour saved by removing the click. Not every workflow should be closed-loop.
Common questions
It designs and builds systems where a language model plans and carries out multi-step work against your real tools and data — defining the tool surface, managing state, adding guardrails and human checkpoints, and building the evaluation harness that proves the agent works before it is trusted with anything important.
A production pilot covering one workflow typically lands in the range of a mid-sized software project rather than a research budget: a week of discovery, four to eight weeks of build, then hardening. The main cost drivers are how many systems the agent must integrate with and whether it acts autonomously or asks for approval — supervised agents are substantially cheaper to make safe.
Sometimes. Frameworks save time on orchestration boilerplate and cost time when you need behaviour they did not anticipate. For a first agent with a narrow tool surface, plain code is often faster to build and far easier to debug. We choose per project rather than by default.
Layered: a narrow tool surface so destructive operations are simply not callable, permissions enforced server-side rather than by prompt, human approval gates on irreversible actions, and output guardrails. Prompt instructions alone are not a security control and we do not treat them as one.
That is the normal case. Agents are most valuable over systems you already run — ERP, CRM, ticketing, data warehouse — because that is where the manual work is. We wrap those in typed tools rather than asking you to migrate anything.
Whichever measures best for the workload, behind an abstraction that lets you switch. We route per task on cost, latency, and accuracy across Claude, GPT, Gemini, and open-weight models.
Related
The ground-up engineering walkthrough behind this service.
The five components every agent shares, with a diagram.
LangGraph, CrewAI, AutoGen — what each is good at.
The wider AI engineering practice this sits inside.
Let's build
Whether you're testing a hypothesis or scaling an established product, we'd be glad to spend a half-hour helping you think through the next step — no pitch deck required.
