Agentic AI with a defined blast radius and an audit log
An agent that can only chat is a chatbot. An agent that can act is a piece of production infrastructure with write access to your systems, and it needs to be built like one: scoped permissions, idempotent tools, an approval gate before anything irreversible, a step limit so it cannot loop forever, and a log of every decision. We build agents that do real work and can be held accountable for it.
You probably need this if
- It has more access than it needs
- It loops and burns budget
- Nobody can reconstruct what it did
- It acts on untrusted input
Where these projects go wrong
Four failure modes we see repeatedly. Knowing them up front is most of the battle.
- 01
It has more access than it needs
The agent runs with a broad API key because scoping was fiddly. One bad instruction now reaches things it was never meant to touch.
- 02
It loops and burns budget
No step limit and no termination condition, so a confused agent retries the same failing call four hundred times overnight.
- 03
Nobody can reconstruct what it did
A record changed and there is no trace of which step, which tool call or which reasoning led there.
- 04
It acts on untrusted input
The agent reads an email or a web page and treats the text inside as an instruction. Prompt injection is not theoretical once an agent has tools.
- Tool definitions with least-privilege scopes
- Idempotent, retry-safe tool implementations
- Approval gates on irreversible actions
- Step, time and spend limits per run
- Instruction-versus-data boundary handling
- Full trace log of steps, tool calls and outcomes
- Evaluation suite over multi-step task scenarios
- Operator console for monitoring and intervention
The engagement, step by step
- 01
Decide what it may never do alone
Sending external messages, moving money, deleting records. That list is written first and enforced in code, not in a prompt.
- 02
Build the tools before the agent
Each capability is a well-typed, idempotent function with its own tests. A reliable agent is mostly reliable tools.
- 03
Run it shadowed
The agent proposes and a human approves for the first period, which produces both a safety record and a real evaluation set.
- 04
Widen autonomy by evidence
Approval gates are relaxed one action at a time, on measured performance, rather than turned off at launch and hoped about.
Agents that act — inside a blast radius
Not chatbots with extra prompts. Each agent has tools, a step budget, an audit trail, and an approval gate before anything irreversible.
Browse agent shapes we ship. The console shows a real run pattern — plan, tool calls, results, gates.
Research & RAG Agent
run · live mock · step budget 12
Break question into retrieval queries over policy + product docs.
query="refund window unused seats" top_k=6
3 hits — Refund Policy §2, Billing FAQ, Contract Addendum A.
Unused seats refundable within 14 days (Refund Policy §2). Cited. Confidence high.
Tools in scope
- vector_search
- fetch_doc
- cite
Reads your corpus, cites sources, and stops when confidence is low — not a hallucinating essay writer.
- Cited retrieval
- Source links in answers
- Refuse when out of corpus
Benefits & business outcomes
What changes when this work is done properly — not activity metrics, business results.
- 01
Multi-step work finished without babysitting
Agentic AI that chains tool calls across your systems — reading records, creating tickets, updating status — inside a bounded task definition.
- 02
A blast radius you can explain to compliance
Least-privilege tool scopes, approval gates on irreversible actions and a full trace log of every step the agent took.
- 03
Autonomy widened by evidence, not hope
Agents start shadowed with human approval, then earn wider permissions one action at a time against measured performance.
- 04
Spend and loops under control
Step limits, time caps and per-run budgets stop a confused agent from retrying the same failing call four hundred times overnight.
The stack for this work
- Anthropic Claude
- OpenAI
- TypeScript
- Python
- MCP
- Queues
- PostgreSQL
- Redis
- Teams automating multi-step back-office work
- Companies with tools and APIs worth orchestrating
- Operations leads drowning in repetitive triage
- Products adding an agent to an existing platform
Working remotely with teams in United States, United Kingdom, Canada, Europe, United Arab Emirates, Saudi Arabia, Singapore, Australia, New Zealand and India.
Industries we serve
The same engineering patterns apply across sectors — the domain language and constraints change.
- Operations and back-office teams
- Customer support and success
- Financial services and fintech
- Insurance and claims processing
- Logistics and supply chain
- HR and internal IT
- SaaS platforms with rich APIs
Why choose BuildsInfinity
Practical reasons teams choose BuildsInfinity for agentic ai development — grounded in how we actually work, not a slogan list.
- 01
Agents built as production infrastructure
Write access to your systems is treated like any other privileged service — scoped permissions, idempotent tools and audit logs, not a prompt asking it to behave.
- 02
Prompt injection treated as the main threat
Instruction-versus-data boundaries, untrusted content handling and approval gates on outward-facing actions — because an agent with tools is a different risk class than a chatbot.
- 03
Reliable tools before clever reasoning
Each capability is a well-typed, tested function. A dependable agentic AI system is mostly dependable tools with orchestration on top.
- 04
Works with the systems you already run
Thin wrappers over your existing APIs and databases, increasingly exposed via MCP so the same tool set serves different agents without rebuilding.
Related services
AI Product Development
Concept to production AI product — model choice, evaluation, cost ceiling and an interface that makes the output checkable.
Generative AI Development
Systems that draft content, code or structured data at volume — constrained to your format, validated, and reviewed before publication.
RAG Development
Answers grounded in your own documents — with hybrid search, reranking, citations and a measured retrieval accuracy score.
Need agentic ai development?
Send a paragraph about the problem and you will get a considered reply from an engineer — a shape, a rough timeline and an honest view on whether we are the right fit. Request a consultation or project estimate anytime.