Coding AI Agent Development
FISTA Solutions deploys coding agents inside real engineering organizations: running framework and dependency migrations, generating tests to close coverage gaps, assisting code review, clearing maintenance backlogs, and documenting legacy code — all behind human review gates and CI verification.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does a software engineering AI agent do?
Coding agents execute bounded engineering work with verification: mechanical migrations across many files, test generation targeting real coverage gaps, review comments on convention and risk, dependency and maintenance tasks, and documentation of code nobody has touched in years.
- 01
Migration agent
Runs framework, API, and dependency migrations across many files with tests proving behavior is preserved.
Migration - 02
Test generation agent
Targets real coverage gaps with meaningful tests, not assertions that merely execute lines.
Quality - 03
Review assistance agent
Comments on convention, risk, and missing tests before a human reviewer spends attention.
Review - 04
Maintenance backlog agent
Clears dependency updates, lint debt, and small defects that never reach a sprint.
Maintenance - 05
Legacy documentation agent
Documents behavior and interfaces of code with no living author, verified against tests.
Knowledge
Requirements
What guardrails does a software engineering agent need?
Coding agents write code that will run in production, so the guardrails are the ones good engineering already uses: human review before merge, CI as the gate, scoped permissions, and a clear record of what the agent changed and why.
| Guardrail | Why it matters | How FISTA implements it |
|---|---|---|
| Human review | Generated code can be subtly wrong. | No direct merges: agents open pull requests that a human reviews and approves under your existing process. |
| CI verification | Tests are the objective check. | Full test suite, type checks, and linters run on every agent change, with failures blocking the pull request. |
| Scoped permissions | Repository access is sensitive. | Least-privilege tokens, branch protection respected, no force pushes, and no access to secrets or production. |
| Bounded scope | Open-ended agent work produces unreviewable diffs. | Task-scoped changes with size limits, so reviews stay tractable and regressions stay traceable. |
| Traceability | Teams must know what the agent did. | Every change linked to its task, prompt version, and model, with the agent's role visible in the pull request. |
Where AI fits
Where should a software engineering agent start?
Start with a mechanical migration or a test-coverage gap. Both have objective success criteria, a large volume of similar work, and a CI gate that catches errors, which is exactly where agents outperform manual effort.
- 01
1. Pick mechanical, verifiable work
Migrations and test coverage have objective criteria and existing verification.
- 02
2. Keep diffs reviewable
Bound each task so a human can review the change in minutes, not hours.
- 03
3. Let CI be the gate
Tests, types, and linters decide what is acceptable, not the agent's confidence.
- 04
4. Measure review burden
If review time exceeds the time saved, narrow the scope rather than pushing more volume.
- 05
5. Expand to judgment-light work
Maintenance and documentation follow once review overhead is proven low.
Cost and timeline
How much does a software engineering agent cost, and how long does it take?
Cost is driven by repository complexity, test coverage, and review capacity; timeline by CI reliability and codebase readiness. FISTA does not quote blind: the scoping call returns a deployment plan, a verification design, and a phased estimate.
Existing test coverage determines what agents can safely do. In a codebase with strong tests, agents can move quickly because the gate catches errors; with weak coverage, test generation is often the first task precisely to build that gate.
Review capacity is the real constraint. FISTA sizes agent throughput to what your team can review, because unreviewed agent output is technical debt with better grammar.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI agent into production?
FISTA delivers agents in four gated phases: a discovery sprint that picks the workflow and writes the agent specification, a design that names tools, permissions, and approval points, a build with an evaluation harness and shadow runs on real work, and a production release with traces, dashboards, and rollback.
- 1
Select and specify
Choose the workflow with a measurable outcome, map its systems and edge cases, and write the agent spec with success metrics.
OutputAgent specification, golden test set
- 2
Design the guardrails
Tool inventory with least-privilege scopes, approval gates, escalation paths, data handling, and the evaluation plan.
OutputTool and permission matrix
- 3
Build and shadow-run
Implement tools as MCP servers or connectors, iterate against the evaluation harness, and run in shadow mode on live inputs.
OutputShadow-mode results, eval scores
- 4
Release and observe
Graduated rollout, full traces, cost and quality dashboards, on-call runbook, and a change process that re-runs the evals.
OutputProduction agent with SLOs
Why FISTA
Why build your software engineering agent with FISTA Solutions?
FISTA deploys coding agents the way it writes code: specification first, review gates, and CI as the arbiter. FISTA is an official Anthropic partner and runs this model on its own delivery work, not only for clients.
Coding Agents specifics
- Agents open pull requests; humans review and merge under your existing process, with no direct writes to protected branches.
- CI is the gate: tests, type checks, and linters must pass before a change is reviewable.
- Task scope is bounded so diffs stay reviewable in minutes and regressions stay traceable.
- Every change records its task, prompt version, and model, so the team always knows what the agent did.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What teams ask before deploying agents.
Straightforward guidance for evaluating scope, fit, and the next step.
01Will coding agents replace our engineers?
They change what engineers spend time on: less mechanical migration and boilerplate, more design, review, and judgment. FISTA reports measured throughput and review burden honestly; staffing decisions are yours.
02Can agents merge code directly?
No. They open pull requests that humans review under your existing process, with CI gating everything. Direct merges remove the verification that makes agent code safe.
03What if our test coverage is poor?
Then test generation is usually the first task, because coverage is what lets later agent work be verified rather than trusted. That sequencing is deliberate.
04How do you keep agents out of secrets and production?
Least-privilege repository tokens, no access to secret stores or production environments, branch protection respected, and full logging of agent actions.
05How quickly does this pay off?
Bounded migrations and coverage work usually show measurable throughput within the first sprints. Sustained value depends on keeping review burden low, which is measured from the start.
Continue exploring
Related capabilities
Scoped in writing before you commit
Clear the mechanical backlog your team keeps deferring.
Bring the migration, the coverage gap, or the maintenance debt. The scoping call returns a deployment plan and a verification design.