Agent demos are quick to build and hard to trust, and closing that gap is engineering work most teams have not staffed for.
Hire Agentic AI Developers for Autonomous Agents, Multi-Agent Workflows & Process Automation
Engineers who build agents that call tools, follow a plan and stop when they should. They treat evaluation, permissions and cost as part of the build, not as cleanup afterwards.
- Tool use, planning loops and memory designed with care
- Evaluation sets and guardrails written alongside the agent
- Works with the model provider and framework you choose
- You meet the engineer in an interview before you sign
- DaysTo a shortlist
- MonthlyContract terms
- YoursCode and IP
Teams That Build With SyntecHire
References available on request.
What You Can Build With Agentic AI
Task Automation Agents
Agents that carry out multi-step back-office work such as ticket triage, record matching or report preparation, with a person approving the risky steps.
Tech Stack
- Python
- LangGraph
- OpenAI API
- PostgreSQL
Outcome
Routine steps run on their own. People review the exceptions.
The Model Context Protocol is an open standard, introduced by Anthropic, for connecting AI applications to external tools and data sources.
Technical Expertise Our Agentic AI Developers Bring
Choosing between a fixed workflow and an open-ended loop, and keeping the design as simple as the task allows.
- Planning
- ReAct
- State Machines
Clear tool schemas, validated inputs and error messages the model can act on when a call fails.
- Function Calling
- JSON Schema
- MCP
Supervisor and handoff patterns, shared state and limits on how long agents may talk to each other.
- LangGraph
- AutoGen
- CrewAI
Deciding what an agent should remember, what it should look up and what should be dropped from context.
- Vector Search
- pgvector
- Context Management
Test cases drawn from real tasks, automated graders and trace review, run on every change.
- Evals
- Tracing
- Regression Tests
Least-privilege credentials, approval steps for actions that cannot be undone and defences against prompt injection.
- Human Approval
- Sandboxing
- Prompt Injection
Smaller models for simple steps, cached prompts and hard limits on steps and tokens per run.
- Model Routing
- Prompt Caching
- Token Budgets
Queues, retries, idempotent actions and logs, because an agent is still software that has to run every day.
- Python
- TypeScript
- Queues
How Agentic AI Has Evolved
- 2022
ReActResearch
The ReAct paper described a loop in which a language model alternates between reasoning and acting through tools.
- 2023
AutoGPTOpen source
AutoGPT drew wide attention to agents that pursue a goal over many steps without a prompt at each one.
- 2023
Function callingTool use
OpenAI added function calling to its API, giving models a structured way to request a tool call.
- 2024
Model Context ProtocolOpen standard
Anthropic introduced MCP as an open protocol for connecting models to tools and data.
When Agentic AI Is The Right Choice
Choose Agentic AI when
- The work has many steps and varies by caseWhere a fixed script breaks on every exception, an agent can choose the next step from what it finds.
- Your systems already have APIsAgents act through tools. If the systems can be called from code, an agent can be given access to them.
- Mistakes can be caught and reversedDrafts, approval steps and undoable actions make it safe to let an agent do the first pass.
- You can say what good looks likeWith examples of correct results, the agent can be measured and improved instead of judged by feel.
Consider something else when
- The process is fixed and predictableA plain workflow or script is cheaper, faster and easier to audit.
- A single model call does the jobClassification, extraction and summarising rarely need an agent around them.
- A wrong action cannot be undoneWhere an error is costly and permanent, keep a person in charge of the action itself.
- There is nothing to test againstWithout sample tasks and expected results, nobody can tell whether the agent is improving.
Why CTOs Choose Us
We look for engineers who have run LLM features for real users and dealt with what goes wrong.
You interview the person who will build the system, and ask about their past work, before any contract.
Work happens in your repositories and under your model provider accounts. Prompts, code and evaluation data belong to you.
The engineer is not shared with other clients, so the context of your agents stays in one head.
NDA before access, month-to-month terms, no exit fee and a replacement if the fit is wrong.
How Your Developer Joins The Team
- 1Step 1
Discovery
A call about your stack, team and the work ahead.
- 2Step 2
Matching
A shortlist of people who have done similar work.
- 3Step 3
Onboarding
Access, environment setup and a first small task.
- 4Step 4
Shipping
Regular pull requests inside your review process.
AI In Delivery
Where coding assistants help, and where a person decides.
Used for speed
Boilerplate, test scaffolding and first drafts of documentation.
Always reviewed
Every change is read and approved by an engineer before it merges.
Never given secrets
Credentials and client data stay out of prompts. Your policy on AI tools applies.
Security And IP
What we enforce on every engagement.
IP assignment
All work product is assigned to you in the contract.
NDA first
Signed before anyone sees your code.
Least privilege
Access limited to what the task needs.
Clean exit
Access revoked and handover documented when work ends.
Agentic AI Developer Pricing Tiers
- Early career
Entry
On request
- Works on defined tasks
- Pairs with a senior reviewer
- Suited to well-scoped backlog items
- Mid level
Experienced
On request
- Owns features end to end
- Reviews others' code
- Suited to most product teams
- Senior
Expert
On request
- Leads architecture decisions
- Mentors the team
- Suited to complex or legacy systems
Rates depend on seniority, stack and team size. We quote a fixed monthly figure per developer.
What Is Included In The Rate
| SyntecHire dedicated developer | Typical freelancer | |
|---|---|---|
| Screening and onboarding | Included | Your time |
| Replacement if the fit is wrong | Included | Start again |
| Continuity and handover notes | Included | Varies |
| IP assignment and NDA | In the contract | Varies |
| Availability | Full time on your product | Shared across clients |
Is A Dedicated Developer The Right Fit?
Five quick questions.
1Is the work expected to run for three months or longer?
2Do you have someone who can set priorities each week?
3Is there an existing codebase or a clear specification?
4Do you want the developer inside your own tools and reviews?
5Would losing context between contractors hurt the project?
0 of 5 answered. Answer every question to see the result.
Related Insights
View All Articles- HiringHow to brief a remote developer so week one is productiveA short checklist covering access, context and a first task that is small enough to finish.
- Project ManagementStaff augmentation or a dedicated team: a decision you can make in ten minutesFour questions about ownership, runway and management time that settle the choice.
- ModernisationSigns your legacy platform has become a business riskWhat to look for in release frequency, incident history and hiring difficulty.
Agentic AI Hiring Questions
They work with the APIs of the major model providers and with common frameworks such as LangGraph and LlamaIndex. Where plain code is clearer than a framework, they use plain code. Tell us your current setup and we shortlist for it.
No, and nobody can honestly promise that. The engineer builds an evaluation set from your real tasks, measures the agent against it and reports the results. You decide when it is ready for use.
Often a simpler workflow is the better answer. A good engineer will say so, and will add agent behaviour only to the steps that need judgement.
An NDA is signed before any access. The engineer works inside your environment, under your model provider agreements, with the permissions you grant.
You do. The contract assigns all work product and IP to you, and everything is stored in your repositories.
Yes. Agents depend on your APIs and data, so the engineer joins your team's tickets, reviews and release process.