AI Agent Development Services
We build AI agents around day-to-day operating work: reading inputs, extracting facts, drafting outputs, checking rules, updating systems, and asking a person to approve anything the agent is unsure of. Your team can see what the agent did, correct it, and decide how much it is allowed to do.
Bounded agents for operating work
Best suited toTeams with a recurring document or data workflow, an accountable owner, representative examples, and a clear definition of a reviewable output.
Not designed forUnbounded autonomous agents, unsupervised high-impact decisions, or a chatbot expected to compensate for missing process and source ownership.
AI that works inside your process.
Document workflow agents
Agents that do the work around documents: read drawings, compare tender packs, extract clauses, organise claim evidence, summarise compliance gaps and prepare outputs for review.
AutoBOQ shows the pattern on construction drawings: inconsistent inputs become quantities, assumptions and checks that an estimator can verify.
Human review and guardrails
High-value workflows need controls. We design approval steps, confidence thresholds, citation trails, role-based permissions, and escalation paths so teams know what the agent did and what still needs judgment.
The agent can work quickly without treating every output as final. Risky decisions stay visible and reviewable.
Systems integration
Deployment means the workflow reaches the tools your team already uses: project folders, spreadsheets, CRMs, ERPs, document stores, email, internal dashboards, and databases.
We handle authentication, rate limits, file formats, retries, and failure modes so the agent survives real operations rather than staying a standalone demo.
Evaluation and monitoring
We record each run: the decisions taken, the document versions used, extraction quality, reviewer overrides and failed cases.
Those records drive improvement after launch: better prompts, rules and data, measured against the same tests each time.
What the engagement includes
This service turns a defined workflow into an inspectable agent system. The agent receives narrow tools and permissions, works from approved context, records what it did, and escalates decisions that exceed its evidence or authority.
Delivery process
- 01
Define state and actions
We document the trigger, inputs, intermediate state, allowed actions, outputs, failure paths, and the person responsible for the final business outcome.
- 02
Connect approved context
The workflow is connected to the minimum necessary files, databases, APIs, or business rules with explicit permissions and source attribution.
- 03
Build controls and evaluation
Confidence thresholds, human approvals, test examples, audit traces, retries, timeouts, and recovery behaviour are designed alongside the successful path.
- 04
Release and observe
A controlled rollout measures task completion, reviewer overrides, failure types, latency, and business outcomes before permissions or volume expand.
Deliverables
- Agent workflow specification with tools, state, permissions, and escalation paths
- Working product flow integrated with agreed systems
- Evaluation set and acceptance criteria based on representative cases
- Run logs, error handling, recovery, and human-review controls
- Deployment, operating, and improvement documentation
Measurement and engagement shape
Useful metrics depend on the workflow: review time, completion rate, extraction quality, override rate, unresolved exceptions, or turnaround time. We agree the metric and baseline before presenting an improvement claim.
The proposal separates discovery, build, integration, and post-launch operation. Timing depends on access to source systems, permission design, evaluation material and the number of exception paths, more than on the model or the interface.
Evidence and preparation
Questions about AI Agent Development Services
What is an AI workflow deployment?
An agent built into an existing workflow rather than a standalone chatbot. It reads documents, extracts facts, drafts outputs, checks rules, updates systems, and passes uncertain cases to a person for review.
How do you keep document agents reliable?
We use approval workflows, confidence thresholds, source citations, reviewer overrides, role-based permissions, evals, and observability. The goal is a controlled workflow where risky decisions remain visible.
What systems can these workflows integrate with?
Project folders, spreadsheets, CRMs, ERPs, document stores, email, internal dashboards, databases, and APIs. We handle auth, file formats, retries, and failure modes needed for production.
Go deeper with the free course:
AI Native Operating SystemOther services
- AI Strategy & RoadmappingFind out where AI is worth using in your work, what is technically realistic, and what to test first.
- RAG Development & AI Data InfrastructurePipelines, retrieval, evaluation and monitoring that keep AI accurate on your documents after launch.
- Rapid AI MVPA 28-day sprint that proves one AI workflow on your own files before you commit to a larger build.
Start with one workflow.
Tell us which piece of work you’d like AI to take on. We’ll reply with what a system could do, what it should leave to a person, and what a first version would take to build.