AI Agents

AI Agent Development

LangGraph · CrewAI · MCP tool routingEval harnesses · policy guardrails · tracesSenior engineers · deeptech delivery since 2017

OQTACORE provides ai agent development for teams that need senior product thinking, real engineering depth, and accountable delivery — from first scope conversation through launch and beyond.

Get a partner who can design, build, integrate, ship, and operate ai agent development as part of a real product, not as an isolated deliverable.

See all ai agents services

Engagements typically run 5–14 weeks · Scoped from a bounded pilot workflow to a full multi-agent program.

Since 2017Deeptech expertise in finance, healthcare, biotech

LangGraph · CrewAI · MCPAgent frameworks we ship with

50+Full-scale apps shipped

Senior-onlyNo juniors learning on your project

Working alongside

What it is

Defining ai agent development

AI agent development is the discipline of planning, building, and operating LLM-driven systems that take bounded actions across APIs, databases, and internal tools, with explicit success criteria, measurable behaviour, and controlled side effects rather than unconstrained chat.

OQTACORE delivers AI agent development where orchestration graphs, tool contracts, and release hygiene are designed before prompts go to production, so teams avoid silent regressions, tool-abuse paths, unlogged actions, and undeclared data egress that break real deployments.

What you get

What OQTACORE delivers in ai agent development

Senior engineers, security-aware architecture, and an operations-ready handoff. Every engagement is scoped to your specific product, chain, and timeline.

Discovery first

Map goals, users, constraints, integrations, and risks before code. We scope to outcomes, not deliverables for their own sake.

Senior design and engineering

No juniors learning on your project. The team that scopes the work is the team that ships it.

Security and reliability built in

Threat modeling, secure patterns, code review, and automated checks so launching feels safe instead of nervous.

Real product, not a deliverable

Frontend, backend, integrations, observability, and operations are designed alongside, not bolted on at the end.

Ship to production

Deployment scripts, environments, CI/CD, monitoring, alerting, and rollback strategy from day one.

Stay after launch

We support what we ship: tuning, fixes, on-call, analytics, and a clear handover plan when you take it in-house.

How production agents are wiredGraphs coordinate steps; tools expose bounded capabilities; evals gate every release.

How we work

A six-phase ai agent development delivery you can plan around

Predictable milestones, clear ownership, and a security pass on every meaningful change. No mystery between scoping and launch.

Discovery and threat model

Map assets at risk, user roles, integrations, regulatory context, and acceptance criteria so we agree on what success looks like before any code is written.

Architecture and scope

Choose chain, language, contracts, services, and integrations. Lock in scope, milestones, ownership, and how third-party teams plug into the build.

Implementation

Senior engineers ship in short cycles with code review on every change, security checklists per module, and tests written next to the code that needs them.

Internal security review

We re-read the code as adversaries: reentrancy, oracle and MEV exposure, access control, accounting precision, upgrade safety, and operational keys.

Testnet and staging

Deploy to testnets and staging environments with full frontend, indexer, and monitoring integration. Fix what only shows up under realistic conditions.

Mainnet launch and run

Coordinate audit findings, plan rollout, deploy with verification, set up monitoring and alerts, and stay on for the first weeks of production.

Want LangGraph-ready engineers on the call?

Tell us about your product, chain, timeline, and the outcome you need. We will reply within one business day with a clear next step — a scoping workshop, an audit, or a delivery plan.

Start a conversation

Five fields. We respond within one business day.

Technology

The stack we use for ai agent development

We pick tools because they make the product safer, faster, or easier to operate — not because they are trending. Here is what tends to show up in ai agent development work.

Next.js

React

Node.js

TypeScript

Python

PostgreSQL

AWS

Docker

Kubernetes

OpenAI

Chains we ship to

Ethereum Arbitrum Polygon BNB Chain

How they differ

Production AI agent development vs. demo chatbots

Both can answer questions in a UI. Only production AI agent development accepts that tools, data, and incentives will be stressed once actions leave the chat transcript.

Dimension

Production AI agent development

Demo chatbots

Outcome

Success metrics, offline and online evals, and regression sets tied to releases.

Subjective replies with no fixed acceptance bar; behaviour drifts unnoticed.

Tooling

MCP endpoints or explicit REST schemas with typed inputs and documented failure modes.

Ad hoc prompts fetching pages or APIs with weak error handling once load appears.

Guardrails

OWASP LLM Top 10–informed reviews, secrets hygiene, PII handling, and policy tests in CI.

Prompt-only mitigations that ignore data leakage and tool-abuse cases.

Operations

Structured tracing, sampling dashboards, on-call runbooks, and rollback for prompts and tools.

Plain chat logs without end-to-end traces for debugging production incidents.

Side effects

Human approval, budgets, and kill switches for mutating actions before money or records move.

Writes executed immediately from chat without enforced controls or audit trails.

Outcomes

What ai agent development delivers in production

Regressed behaviour caughtOffline and online eval suites with versioned datasets

Tool calls constrainedAllowlists, schemas, OWASP LLM Top 10 reviews per release

Runs traced end to endStructured logs plus trace IDs across LLM and tool calls

Side effects governedHuman approvals, rate limits, kill switches for writes

Standards we work to

References our ai agent development engagements rely on

We treat these documents as the working baseline for design, testing, and review. Linking out so you can verify the reasoning instead of taking our word for it.

LangGraphStateful multi-step orchestration patterns we implement against your tool and data graph.Model Context ProtocolStandard tool and resource wiring we align with when connecting agents to backends.OWASP Top 10 for LLM ApplicationsThreat classes we enumerate in design reviews and acceptance tests for releases.OpenAI API documentationCanonical reference for chat, tool, and structured-output behaviour we integrate to.Anthropic API documentationMessages, tool use, and safety fields we map into agent policies where models fit.

Where AI agent development with OQTACORE pays off

Operations-heavy workflows with repetitive decisions — ticket triage, reconciliations, exception handling, internal copilots that file updates — gain the most when each step emits traces and passes the same evals after model upgrades. Consumer-style chat without actions rarely needs this rigour; regulated and revenue-bearing processes do.

OQTACORE pairs agent work with the broader product surface when needed: APIs, web apps, data pipelines, and observability stacks so the agent is not a sandbox script. Across more than fifty full-scale applications delivered, we staff senior engineers only, and our AI chapter ships with the LangGraph, CrewAI, and MCP patterns your stakeholders already ask for by name.

How an AI agent development engagement starts

We run a compact scoping workshop on target workflows, risk appetite, available APIs, and model choices, then return a milestone plan with eval acceptance thresholds and a week-by-week runway — typically five to fourteen weeks depending on workflow count and integration depth.

You can start with a single bounded workflow and MCP or REST tool surface, expand into additional LangGraph subgraphs or CrewAI crews, or ask for remediation when an existing agent misbehaves under load. Each option keeps the same bar: passing evals, showing traces, and shipping guardrails before production traffic.

Related work

Services teams usually combine with ai agent development

Most engagements pair ai agent development with a few neighbours. Open the ones closest to your project to see how we deliver them.

Multi-Agent System Development

Coordinated agent teams with planning, delegation, memory, and supervision for complex tasks.

Learn more

Autonomous AI Agents

Long-running agents that plan, retry, recover, and execute multi-step business workflows with minimal human oversight.

Learn more

Voice AI Agents

Real-time voice agents for sales, support, scheduling, and operations using LiveKit, Vapi, Retell, and custom stacks.

Learn more

Coding AI Agents

Software engineering agents that read code, plan changes, run tests, and ship pull requests inside your repos.

Learn more

MVP Development

Launch a focused MVP with product discovery, UX, backend, integrations, analytics, and investor-ready delivery.

Learn more

Blockchain Development

Build blockchain networks, protocols, integrations, wallets, data services, and production Web3 applications.

Learn more

FAQ

AI Agent Development — questions before you start

The answers most teams ask for before scoping a project with us.

What is included in ai agent development?

Scope depends on your goals, but engagements typically include discovery, architecture, implementation, integrations, QA, deployment, documentation, and post-launch support.

Can OQTACORE work with our existing team?

Yes. We can operate as a dedicated squad, augment your internal team, own a specific workstream, or provide senior consulting around architecture and delivery.

How do you estimate timeline and budget?

We start with a technical scoping session, identify risks and dependencies, then define milestones with acceptance criteria. Estimates are tied to outcomes rather than vague hours.

Do you support launch and post-launch improvements?

Yes. OQTACORE can support launch, monitoring, analytics, performance improvements, feature iteration, and long-term product evolution.

Ready when you are.

Send a few lines about your project. We will reply within one business day with a clear next step — a scoping workshop, a security review, or a delivery plan with milestones.

Prefer a longer brief or want to share an NDA before we exchange details? Mention it in the message and we will route it appropriately.

Engagements typically run 5–14 weeks · Scoped from a bounded pilot workflow to a full multi-agent program.

Page last reviewed May 7, 2026

Start an AI agent development engagement

One business day reply. NDA on request.