// AI Agent Development
Agents that actually do the work.
We build multi-step AI agents that act on your systems, tools, and data — with the guardrails, evaluations, and observability production demands. No demo-only agents that collapse under real traffic.
// Who this is for
Built for teams who are past the experiment phase.
CTOs at Series A–C SaaS scale-ups replacing brittle workflow automation with tool-using agents.
Mid-market CEO/COOs looking to shift operational headcount off repetitive back-office work without losing auditability.
AI-native founders who need a design partner to turn a working prototype into a reliable product agents can run 24/7.
// What we deliver
The scope, in plain language.
Every engagement is scoped against your business outcome, not a fixed menu. What you see below is the typical shape — we tighten it with you in the first week.
- Agent architecture design: single-agent, multi-agent, or graph workflows — selected against your latency, cost, and reliability budget.
- Tool and function definitions wired to your APIs, databases, and internal systems with typed contracts.
- Guardrails: input validation, permission scopes, cost ceilings, and human-in-the-loop breakpoints for high-risk actions.
- Evaluation harness with task-level test sets, regression checks, and CI gates so you can change models without regressing behavior.
- Observability: full trace capture, token and cost accounting per run, and replay tooling for production incidents.
- Deployment to your cloud with rollback, shadow mode, and staged traffic ramp.
- Operator handoff: runbooks, on-call guidance, and a 30-day warranty on the first production release.
// How we work
The Ankor 7-stage framework, applied to ai agent development.
- 01Discover
Align on business outcome, constraints, and success metric.
- 02Define
Pin down scope, architecture, and the evaluation bar.
- 03Design
Model, data, and UX design — with trade-offs on the table.
- 04Data
Audit, remediate, and pipe the data the build actually needs.
- 05Develop
Ship the system in small, testable increments against the eval bar.
- 06Deploy
Rollout with shadow mode, guardrails, and rollback.
- 07Drive
Operate, measure, and iterate — handoff or retainer.
// Outcomes you can expect
Ranges, not guarantees. Specific, not boastful.
Cycle-time reduction on first target workflow once the agent is in shadow-mode-cleared production.
From kickoff to a production agent running on real traffic, not a demo dataset.
Every agent invocation logged with inputs, tool calls, costs, and decisions — auditable by default.
// Why Ankor
A decade of shipping software, repointed at production AI.
- 10
- years shipping software
- 190+
- clients delivered
- 260+
- products shipped
- 800K+
- daily users served
Serving clients across APAC, the US, and EMEA.
Why most agent projects fail
Agent prototypes look magical. Agent production is a different sport. The failure modes are boring: unreliable tool calls, prompt regressions on model updates, cost blowouts from runaway loops, no way to reproduce what the agent did on a given run, and no path to improve it without rewriting everything.
We build agents the way we build any other production system — with typed contracts, test suites, observability, and a runbook. The “intelligence” is one layer. The surrounding scaffolding is what makes it operable.
How we structure the build
Weeks 1–2 we pin down the target workflow, the tool surface, and the guardrails. Weeks 3–6 we build the agent plus the evaluation harness in parallel — the harness is non-negotiable. Weeks 7–9 we run shadow mode against real traffic, tune, and wire up observability. Weeks 10–12 we staged-roll to production with rollback.
After that, your team owns it. We stay on retainer for model upgrades and new tool integrations if useful — not required.
// FAQ
Questions we get a lot.
Which agent frameworks do you use?
Whatever the job actually requires: LangGraph, Temporal, OpenAI Agents SDK, Anthropic's MCP tooling, or a hand-rolled orchestration loop in Python or TypeScript. We choose based on your reliability and ops constraints, not framework preference. We explain the trade-offs in writing.
How do you stop the agent from doing something stupid in production?
Permission scopes on every tool, cost ceilings per run, structured output schemas, and human-in-the-loop breakpoints on irreversible actions. Plus an evaluation harness that runs on every model upgrade. Guardrails are scoped up-front, not retrofitted after the first incident.
Can the agent run against our internal systems?
Yes. We integrate over whatever you have — REST, GraphQL, gRPC, SOAP, database read replicas, event streams. If the system does not have an API, building one is usually the first task in the plan.
What does it cost to run in production?
Highly dependent on model choice and call volume. We instrument token and dollar cost per agent task from day one, and the evaluation harness lets us swap to cheaper models without regressing quality. Most agents we ship land at $0.02–$0.40 per completed task.
Do you support open-weight or on-prem models?
Yes. For regulated or cost-sensitive clients we ship agents backed by Llama, Mistral, or Qwen variants on your infrastructure, with the same guardrails and evaluation setup. See our private LLM deployment service.
// Ready to ship?
Let's talk about what to build first.
Short call. No deck. We will tell you honestly whether we are the right team for your problem.
// Related services
Keep exploring.
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RAG pipelines your legal team signs off on.
Grounded, cited, permission-aware retrieval — with evaluation harnesses that catch regressions before users do.
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AI strategy that survives the roadmap review.
Opinionated roadmaps, build-vs-buy math, and vendor selection from a team that ships production AI.