The first agent gets one job and a result you can inspect. It carries only the tools, knowledge, and authority that job needs, with clear rules for stopping, recovering, or asking a person to decide.

Some of the 500+ brands we've worked with. Our delivery runs on 100+ AI workflows in production.

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Choose from 6 parts of the agent system to define, build, connect, or control. Each offering has a separate method and handoff.

Build & Integrate

Custom AIAgent Development

Bring us one repeatable job with an input your team recognizes and a result they know how to judge. We build the shortest agent loop that can do it, then trace where it finishes, gets stuck, fails, and hands the work back to a person.

A custom agent fits when one repeatable job has a checkable result and needs a state map covering tools, stops, recovery, and human handoff.

AI Agent Tool& API Integration

A timeout can hide a completed action, and the retry can run it again. We define the contract, restrict the agent's authority, and test side effects, failures, audit traces, and the rollback paths that exist before release.

Tool integration is the right branch when the agent loop exists but timeouts, retries, and downstream side effects still lack a versioned call contract.

Knowledge-Grounded AIAgents

An internal document can support an answer without authorizing the next action. We build the agent around named source owners, identity-aware retrieval, visible citations, freshness and abstention rules, then keep tool authority in a separate control path.

Ground the agent when changing internal knowledge must carry source authority, caller identity, citations, and abstention without granting permission to act.

AI Agent Strategy& Architecture

An agent proposal can hide very different systems, from a helper that waits for approval to one allowed to act through tools. Before engineering starts, we define the job, compare the least complex options, and map the tools, memory, permissions, evaluation, escalation, and operating roles each option would require.

Strategy comes first when agent jobs, autonomy levels, tools, memory, and operating roles still need a defensible architecture decision record.

Multi-Agent SystemDevelopment

Some jobs move from research to a tool action and then to evaluation, with no single agent equipped to finish the whole path. We build a bounded system for those jobs, then test stale state, role conflict, stalled work, budget pressure, and the route back to a person before your team releases it.

Multiple agents make sense only when one job crosses distinct specialist roles and needs replayable handoffs, shared-state provenance, and conflict tests.

AI Agent Identity, Permissions& Human Approval Design

For every consequential action, your team needs to know which identity is acting, what it may do, who can approve it, and when that authority ends. We design that path and specify the record it should leave for later review.

Address identity and approvals when an agent can take consequential actions but its least-privilege grants, expiry rules, and human gates remain unsettled.

An agent may call a tool, change a record, or pass work to another system. We make its actions, permissions, evidence, failure paths, and owners as clear as the answers it produces.

We begin with one task and the smallest useful access. Representative and adverse cases show whether the agent can gain another tool, permission, or workflow step, or whether the design should stay narrow.

A task contract is written with the workflow owner before the first tool is connected.

With the workflow owner we write down the input, the acceptable result, the forbidden actions, the data boundary, failure examples, budgets, and the decisions that stay with a person; that contract is what the planning and execution loop, tool contracts, identity and access rules, state, and safe-stop path are built against. Representative, edge, and adverse tasks produce traces we read for completion, tool calls, permissions, cost, latency, stalled loops, recovery, and escalation, and the agent gains another tool, permission, or workflow step only when that evidence supports it. Release happens within agreed limits, with tracing, monitoring, intervention, rollback, and operating ownership connected and new tests written from the failures we actually observed.

We take the work from a testable job to an operated handoff.

Task contract and feasibility
With the workflow owner, we write down the input, acceptable result, forbidden actions, data boundary, failure examples, budgets, and the decisions that stay with a person.
Agent loop and tool boundaries
We build the planning and execution loop, structured tool contracts, identity and access rules, state, memory, and a safe path when the agent cannot continue.
Trajectory and failure evaluation
Representative, edge, and adverse tasks produce traces we read for completion, tool calls, permissions, cost, latency, stalled loops, recovery, and escalation.
Staged release and handoff
We release within agreed limits and connect tracing, monitoring, intervention, rollback, operating ownership, and new tests created from observed failures.

Every operational consultant at Zeo has secure LLM access and training, and AI sits inside the daily work. Five of them came through our AI Bootcamp and wrote down what they expect it to change.

I see generative AI having an enormous effect on daily life and on every industry it touches. As the technology develops, the range of uses will keep widening across creativity, problem-solving, and innovation. We can already see that range in realistic image, video, and music production, pharmaceutical research, and design. I expect the effect on industries to become profound. E-commerce, healthcare, finance, and many other sectors will be able to create more engaging, personalized experiences and make their processes more efficient.

The ability to produce unique content and solutions will open new possibilities and increase efficiency.

Ozan Ketenci

Generative AI has the potential to transform SEO, digital marketing, and many other sectors. I expect it to play an important role in our lives in the near future, with more personal experiences, more effective marketing, faster interpretation of data, and quicker action. Products and services will improve. Processes such as customer communication will become more efficient, and organizations that fail to keep up will fall behind businesses that bring AI into their work.

Organizations should start planning the AI applications that make sense for their sector now.

Samet Özsüleyman

We may be at a moment as significant as the computer revolution, with the potential to transform businesses and industries. Yet for many people, generative AI still means opening a tool such as ChatGPT for a task at work or in daily life. That is only the surface. Companies that integrate generative AI models into workflows and customer processes, and go beyond content production, will gain huge competitive advantages in the coming years.

I believe generative AI should be on the agenda of every board of directors as soon as possible.

Hande Parmaksız

I see artificial intelligence as the most exciting technology of both the present and the future. Its potential is unlimited, and we're still at the tip of the iceberg. AI is developing quickly, while much of what it could mean for different sectors remains unexplored. The effect on digital work is already substantial. In the years ahead, I expect breakthroughs that change how entire industries work.

AI's potential will keep expanding. No sector can afford to ignore the opportunity for efficiency and progress. We will keep discovering new dimensions, and I don't see a saturation point.

Can Mutioğlu

Work by major technology companies is likely to give generative AI a much wider role in the years ahead. It will create new dynamics in art and design, as well as in sensitive fields such as healthcare and finance. As the technology becomes part of daily life, the ethical and risk questions will grow with it. Being able to follow and experience those developments up close is what makes generative AI so exciting to me.

I look forward to seeing more uses of generative AI that benefit society.

Ezgi Gülsen Yaylı

Three speakers look at the pace of AI change and what it means for e-commerce and content teams.

The Future Will Come Faster Than You Imagined

Yiğit Konur, Wope

Effective Use of AI for E-Commerce Companies

Burak Pehlivan, Wope

Producing Content Compatible with Google Algorithms with AI

Mert Erkal, Stradiji

Agents, chatbots, and RAG systems at Zeo are built by senior engineers who keep operating them after launch. The consultants below are those builders, matched to the work this page covers.