AI Agent Developer: Raleigh

Hire an AI agent developer in Raleigh.

The Research Triangle concentrates three categories of technical work that standard agent frameworks do not handle well out of the box. SAS Institute and similar enterprise software companies need agents that interact with internal developer tooling under proper OAuth scopes. Red Hat engineers need agents that query multiple external sources and know which ones to trust. University-affiliated biotech teams need long-horizon research tasks that can pause, checkpoint, and resume without losing hours of work.

Each of these requires deliberate architecture decisions, not framework defaults. Source reliability scoring, hierarchical agent planning, and scoped API authentication all need to be built in, not added after the first production failure.

We build agents with these requirements from the start, with fixed scope and flexible pricing based on the engagement.

How we work

Describe your research or enterprise tooling automation need.

What production research agents require.

A research agent that queries PubMed, patent databases, and regulatory repositories will encounter sources that are wrong, outdated, or retracted. If the agent treats every source as equally reliable, the synthesis it produces inherits every error it encountered. Source reliability scoring assigns a trust tier to each source type before retrieval runs, and the synthesis step is constrained to cite in proportion to that tier.

Academic database APIs rate-limit at 3 to 10 requests per second depending on the provider. A research agent that fires concurrent requests at full speed will receive HTTP 429 errors and get its IP banned within minutes. The agent needs exponential backoff, request queuing, and a result cache keyed by query hash so repeated runs do not re-fetch data that was already retrieved this session.

Long research tasks take 20 to 60 minutes. If the task times out at minute 45, it needs to resume from minute 45, not restart from minute zero. Checkpointing writes intermediate state to a persistent store at each sub-task boundary.

Hierarchical planning and sub-agents.

How a single research goal becomes parallel, supervised work.

Meta-agent decomposition

The meta-agent receives the research goal and breaks it into sub-tasks with defined output types and token budgets. Sub-tasks like 'retrieve all papers citing X' and 'extract competitor patent claims in category Y' run in parallel.

Specialized sub-agents

Each sub-agent is tuned for its task: a literature retrieval agent optimized for PubMed query syntax, a data extraction agent with structured output schemas, a synthesis agent with citation-only instructions. Specialization improves reliability over a single generalist agent.

Decomposition validation

Before sub-agents execute, the plan is checked: each sub-task has a defined output type, a success criterion, and a budget ceiling. Ambiguous sub-tasks are flagged for revision before resources are spent executing them.

Result aggregation

After sub-agents complete, the meta-agent receives their outputs and runs the synthesis step. It knows which sub-tasks completed, which failed, and what source tiers were used, so the final output accurately reflects the evidence base.

Enterprise tooling integration.

OAuth scoped authentication

Agents that interact with Jira, Confluence, and GitHub authenticate on behalf of the triggering user via OAuth delegation. The agent's access is bounded by that user's permissions. A service account with broad access is not an acceptable substitute in an enterprise environment.

Rate limit handling and caching

Every external API integration includes exponential backoff on 429 responses, a request queue to stay within per-minute limits, and a query result cache with configurable TTL. Academic databases like PubMed and Semantic Scholar enforce strict rate limits, the cache is what makes repeated runs feasible.

Source reliability scoring

Each data source is assigned a trust tier at configuration time. Peer-reviewed and government sources get Tier 1. Preprints and grey literature get Tier 2 with a flag. The synthesis step weights citations by tier and surfaces Tier 2 citations for human review.

Research task checkpointing

Sub-task outputs are written to a persistent checkpoint store at each stage. A failed or timed-out task reloads the last checkpoint and continues from there. Completed sub-tasks are not re-executed on retry.

Questions from Research Triangle engineering teams.

How do you build source reliability scoring into a research agent, what signals indicate a source is trustworthy?: We assign a reliability tier to each source type at configuration time. Peer-reviewed journals indexed in PubMed or Scopus get Tier 1. Government regulatory repositories (FDA, USPTO, EPA) get Tier 1. Preprint servers get Tier 2 with a flag. Wikipedia and non-indexed sources get Tier 3. The agent records the source tier on every retrieved fact. Synthesis prompts are instructed to weight Tier 1 sources above Tier 2, and to explicitly surface Tier 3 citations for human review before the output is treated as final.
What does hierarchical agent planning look like in practice. How do you prevent the meta-agent from making bad decomposition decisions?: The meta-agent generates a decomposition plan and then validates it against a set of structural checks before delegating to sub-agents: each sub-task must have a defined output type, a maximum token budget, and a success criterion. If the decomposition produces a sub-task that is ambiguously scoped or exceeds the budget ceiling, the meta-agent is prompted to revise before execution starts. We also run a small eval set during QA that confirms the meta-agent decomposes known research goals into sensible sub-tasks. This catches regressions when you update the planning prompt.
For enterprise Jira and Confluence integration, how do you handle the permissions model, should agents respect the same access controls as the human users?: Yes, and this is non-negotiable for enterprise deployments. The agent authenticates via OAuth on behalf of the user who triggered the task. The Jira and Confluence APIs enforce that user's access scope. The agent cannot read a project the triggering user cannot read, and cannot write to a space the user cannot write to. We do not use a service account with broad permissions. A service account would mean a junior engineer's agent could read the executive board space. OAuth delegation prevents that.
What eval framework do you use for a research agent that produces outputs requiring expert assessment?: We use a two-layer eval. The first layer is automated: structural checks on output format, citation count, source tier distribution, and token budget compliance. These run on every deployment. The second layer is a human-in-the-loop review panel with 10 to 20 gold-standard research tasks that a domain expert scores on a defined rubric (accuracy, citation reliability, coverage of key sub-topics). The panel runs before each major prompt update. We track the score over time so you can see whether a model upgrade or prompt change improved or degraded output quality.

Describe the research task or tooling integration.

Tell us the data sources, the output format, and whether enterprise tooling authentication is in scope. We will reply within one business day.

Hire an AI agent developer in Raleigh.

We build agents with these requirements from the start, with fixed scope and flexible pricing based on the engagement.

What production research agents require.

Hierarchical planning and sub-agents.

How a single research goal becomes parallel, supervised work.

Meta-agent decomposition

Specialized sub-agents

Decomposition validation

Result aggregation

Enterprise tooling integration.

OAuth scoped authentication

Rate limit handling and caching

Source reliability scoring

Research task checkpointing

Questions from Research Triangle engineering teams.

How do you build source reliability scoring into a research agent, what signals indicate a source is trustworthy?

We assign a reliability tier to each source type at configuration time. Peer-reviewed journals indexed in PubMed or Scopus get Tier 1. Government regulatory repositories (FDA, USPTO, EPA) get Tier 1. Preprint servers get Tier 2 with a flag. Wikipedia and non-indexed sources get Tier 3. The agent records the source tier on every retrieved fact. Synthesis prompts are instructed to weight Tier 1 sources above Tier 2, and to explicitly surface Tier 3 citations for human review before the output is treated as final.

What does hierarchical agent planning look like in practice. How do you prevent the meta-agent from making bad decomposition decisions?

The meta-agent generates a decomposition plan and then validates it against a set of structural checks before delegating to sub-agents: each sub-task must have a defined output type, a maximum token budget, and a success criterion. If the decomposition produces a sub-task that is ambiguously scoped or exceeds the budget ceiling, the meta-agent is prompted to revise before execution starts. We also run a small eval set during QA that confirms the meta-agent decomposes known research goals into sensible sub-tasks. This catches regressions when you update the planning prompt.

For enterprise Jira and Confluence integration, how do you handle the permissions model, should agents respect the same access controls as the human users?

Yes, and this is non-negotiable for enterprise deployments. The agent authenticates via OAuth on behalf of the user who triggered the task. The Jira and Confluence APIs enforce that user's access scope. The agent cannot read a project the triggering user cannot read, and cannot write to a space the user cannot write to. We do not use a service account with broad permissions. A service account would mean a junior engineer's agent could read the executive board space. OAuth delegation prevents that.

What eval framework do you use for a research agent that produces outputs requiring expert assessment?

We use a two-layer eval. The first layer is automated: structural checks on output format, citation count, source tier distribution, and token budget compliance. These run on every deployment. The second layer is a human-in-the-loop review panel with 10 to 20 gold-standard research tasks that a domain expert scores on a defined rubric (accuracy, citation reliability, coverage of key sub-topics). The panel runs before each major prompt update. We track the score over time so you can see whether a model upgrade or prompt change improved or degraded output quality.

Describe the research task or tooling integration.

Tell us the data sources, the output format, and whether enterprise tooling authentication is in scope. We will reply within one business day.